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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

Topology Estimation for Smart Micro Grids via Powerline Communications

TL;DR: This paper proposes a technique that allows estimating the SMG topology by exploiting PLC signal itself, thus providing an appealing plug-and-play solution for routing optimization.
Journal ArticleDOI

Time-Frequency Analysis for GNSSs: From interference mitigation to system monitoring

TL;DR: The article describes the fundamental role of TFDs in monitoring the performance of new GNSSs, including satellite clocks and ionospheric scintillation data, and the integration of the spatial domain with the TFDs, through the use of multiantenna receivers, permits the applications of space-time processing for effective jamming mitigation.
Journal ArticleDOI

Optimal Downlink Transmission for Cell-Free SWIPT Massive MIMO Systems With Active Eavesdropping

TL;DR: A fair comparison between the proposedcell-free and the colocated massive MIMO systems shows that the cell-free M IMO outperforms the colocate MIMo over the interval in which the AHE constraint is low and vice versa.
Journal ArticleDOI

Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient

TL;DR: An algorithm is proposed for lowering this complexity significantly by showing that the NLS estimator can be computed efficiently by solving two Toeplitz-plus-Hankel systems of equations and by exploiting the recursive-in-order matrix structures of these systems.
Journal ArticleDOI

P-order metric UWB receiver structures with superior performance

TL;DR: The generalized Gaussian probabilitydensity function is shown to better approximate the probability density function of the multiple access interference in ultra-wide bandwidth systems than the Gaussian approximation and the Laplacian density approximation.
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.