World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
47295
World Ranking
3716
National Ranking
1772

Research.com Recognitions

  • 1989 - IEEE Fellow For contributions to the theory and application of parametric spectral estimation and detection.

Overview

Steven Kay is affiliated with the University of Rhode Island in the United States. Their academic work spans multiple fields, primarily focusing on Engineering, Computer Science, and Physics and Astronomy. Within these fields, their research addresses specialized subfields including Astronomy and Astrophysics, Aerospace Engineering, Signal Processing, Artificial Intelligence, and Electrical and Electronic Engineering.

Their research explores diverse topics such as Astro and Planetary Science, Planetary Science and Exploration, Space Satellite Systems and Control, Direction-of-Arrival Estimation Techniques, Speech and Audio Processing, Radar Systems and Signal Processing, and Neural Networks and Applications.

Steven Kay has contributed several papers to well-known publication venues. Notably, their work appears in IEEE Signal Processing Letters, arXiv (Cornell University), Advances in Space Research, IEEE Transactions on Signal Processing, and IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control.

  • Active debris removal: A review and case study on LEOPARD Phase 0-A mission (2023, Advances in Space Research)
  • Robotic technologies for in-orbit assembly of a large aperture space telescope: A review (2024, Advances in Space Research)
  • Optimal Sparse Sampling for Detection of a Known Signal in Nonwhite Gaussian Noise (2021, IEEE Signal Processing Letters)
  • An Exact Solution for Sparse Sampling for Optimal Detection of Known Signals in Gaussian Noise (2023, IEEE Signal Processing Letters)
  • Nonlinear Dimension Reduction by PDF Estimation (2022, IEEE Transactions on Signal Processing)

The scientist collaborates frequently with several coauthors including Kaushallya Adhikari, Cristina Luna, Angus Cameron, Mithun Poozhiyil, and Manu H. Nair.

Steven Kay was named an IEEE Fellow in 1989 for contributions to the theory and application of parametric spectral estimation and detection.

Best Publications

  • Fundamentals of statistical signal processing: estimation theory

    Steven M. Kay

  • Fundamentals Of Statistical Signal Processing

    Steven M Kay

  • Modern Spectral Estimation: Theory and Application

    Steven M. Kay

  • Spectrum analysis—A modern perspective

    S.M. Kay;S.L. Marple

  • A fast and accurate single frequency estimator

    S. Kay

  • Parameter estimation of chirp signals

    P.M. Djuric;S.M. Kay

  • Fractional Brownian Motion: A Maximum Likelihood Estimator and Its Application to Image Texture

    Torbjorn Lundahl;William J. Ohley;Steven M. Kay;Robert Siffert

  • Digital signal processing for sonar

    W.C. Knight;R.G. Pridham;S.M. Kay

  • Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors

    Hao Chen;P.K. Varshney;S.M. Kay;J.H. Michels

  • Optimal Signal Design for Detection of Gaussian Point Targets in Stationary Gaussian Clutter/Reverberation

    S. Kay

  • Can detectability be improved by adding noise

    S. Kay

  • Toward Optimal Feature Selection in Naive Bayes for Text Categorization

    Bo Tang;Steven Kay;Haibo He

  • On the optimality of the Wigner distribution for detection

    S. Kay;G. Boudreaux-Bartels

  • A Bayesian Classification Approach Using Class-Specific Features for Text Categorization

    Bo Tang;Haibo He;Paul M. Baggenstoss;Steven Kay

  • Noise compensation for autoregressive spectral estimates

    S. Kay

  • The effects of noise on the autoregressive spectral estimator

    S. Kay

  • Waveform Design for Multistatic Radar Detection

    S. Kay

  • Intuitive Probability and Random Processes using MATLAB

    Steven Kay

  • Rethinking biased estimation [Lecture Notes]

    S. Kay;Y.C. Eldar

  • Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling

    S. Saha;S.M. Kay

  • Statistically/computationally efficient frequency estimation

    S. Kay

  • Gaussian Random Processes

    Steven M. Kay

Frequent Co-Authors

Haibo He
Haibo He University of Rhode Island
Petar M. Djuric
Petar M. Djuric Stony Brook University
Pramod K. Varshney
Pramod K. Varshney Syracuse University
Muralidhar Rangaswamy
Muralidhar Rangaswamy United States Air Force Research Laboratory
Petre Stoica
Petre Stoica Uppsala University
Ram M. Narayanan
Ram M. Narayanan Pennsylvania State University
Alfonso Farina
Alfonso Farina Finmeccanica (Italy)
Arye Nehorai
Arye Nehorai Washington University in St. Louis

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