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Fundamentals Of Statistical Signal Processing
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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.read more
Citations
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Journal ArticleDOI
Detection of Periodic Forced Oscillations in Power Systems
Jim Follum,John W. Pierre +1 more
TL;DR: In this paper, an algorithm for the detection and frequency estimation of periodic forced oscillations in power systems is proposed, which operates by comparing the periodogram of synchrophasor measurements to a detection threshold.
Book ChapterDOI
Imaging sensor noise as digital X-ray for revealing forgeries
TL;DR: A new forensic tool for revealing digitally altered images by detecting the presence of photo-response nonuniformity noise (PRNU) in small regions by running the algorithm on a large number of non-forged images.
Journal ArticleDOI
Joint Channel Parameter Estimation via Diffusive Molecular Communication
TL;DR: In this article, the Fisher information matrix of the joint estimation problem is derived so that the Cramer-Rao lower bound on the variance of locally unbiased estimation can be found.
Journal ArticleDOI
Kalman filtering for power estimation in mobile communications
TL;DR: This work proposes a scalar Kalman-filter-based approach for improved local mean power estimation, with only slightly increased computational complexity, based on a first-order autoregressive model of the shadow process.
Proceedings ArticleDOI
An observability-constrained sliding window filter for SLAM
TL;DR: This work proposes an observability-constrained (OC)-SWF where the linearization points are selected so as to ensure the correct dimension of the nullspace of the Hessian, as well as minimize thelinearization errors.
References
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Book
Adaptive Filter Theory
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.