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Fundamentals Of Statistical Signal Processing

Steven Kay
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The article was published on 2001-03-16 and is currently open access. It has received 7058 citations till now. The article focuses on the topics: Statistical signal processing.

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Citations
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Journal ArticleDOI

Weighted Energy Detection for Noncoherent Ultra-Wideband Receiver Design

TL;DR: Simulations show that the proposed noncoherent WED receiver enhances the bit-error-rate performance compared to conventional energy detectors.
Journal ArticleDOI

Positioning with OFDM signals for the next- generation GNSS

TL;DR: Computer simulations verify that the positioning accuracy of the proposed algorithm with bearable complexity can reach the Cramer-Rao lower bound (CRLB) in the range of medium to high signal-to-noise ratio (SNR).
Journal ArticleDOI

Automatic Transcription of Guitar Chords and Fingering From Audio

TL;DR: The method was evaluated on recordings from the acoustic, electric, and the Spanish guitar and clearly outperformed a non-guitar-specific reference chord transcription method despite the fact that the number of chords considered here is significantly larger.
Journal ArticleDOI

The correntropy MACE filter

TL;DR: The correntropy MACE (CMACE) can potentially improve upon the MACE performance while preserving the shift-invariant property and outperforms the linear MACE in both generalization and rejection abilities.
Journal ArticleDOI

Robust Estimators for Multipass SAR Interferometry

TL;DR: This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR, and can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
References
More filters
Book

Adaptive Filter Theory

Simon Haykin
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Journal ArticleDOI

Fundamentals of statistical signal processing: estimation theory

TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Book

Probability, random variables and stochastic processes

TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Book

Probability, random variables, and stochastic processes

TL;DR: In this paper, the meaning of probability and random variables are discussed, as well as the axioms of probability, and the concept of a random variable and repeated trials are discussed.
Book

Discrete-Time Signal Processing

TL;DR: In this paper, the authors provide a thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete time Fourier analysis.