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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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Proceedings ArticleDOI

A comparative study of target detection algorithms for hyperspectral imagery

TL;DR: In this paper, a new target detection workflow incorporating a Minimum Noise Fraction (MNF) transform before target detection was introduced, which improved the detection results in general, especially with the Orthogonal Subspace Projection (OSP) detector.
Journal ArticleDOI

Estimation of Cortical Connectivity From EEG Using State-Space Models

TL;DR: A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG and it is demonstrated that this integrated approach is less sensitive to noise than two-stage approaches.
Journal ArticleDOI

Voltage Analytics for Power Distribution Network Topology Verification

TL;DR: Numerical tests using real data on benchmark feeders demonstrate that reliable topology estimates can be acquired even with a few smart meter data, while the non-convex schemes exhibit superior line verification performance at the expense of additional computational time.
Journal ArticleDOI

Non-Systematic Complex Number RS Coded OFDM by Unique Word Prefix

TL;DR: The concept of non-systematic coded UW-OFDM, where the redundancy is no longer allocated to dedicated subcarriers, but distributed over all sub carriers is introduced, and optimum complex valued code generator matrices matched to the best linear unbiased estimator (BLUE) and to the linear minimum mean square error (LMMSE) data estimator, respectively are derived.
Proceedings ArticleDOI

Joint estimation in sensor networks under energy constraints

TL;DR: The proposed optimal power scheduling scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to conserve power.
References
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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.