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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
Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals
TL;DR: In this paper, a new method for detecting bearing faults using vibration signals is proposed, which is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices.
Journal ArticleDOI
On the Spatial Error Propagation Characteristics of Cooperative Localization in Wireless Networks
TL;DR: The spatial propagation function is proposed to reveal the spatial cooperation principle of network localization, and the convergence property of spatial localization information propagation (SLIP) is analyzed to shed light on the performance limits of network globalization through spatial information propagation.
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Channel Tracking and Transmit Beamforming With Frugal Feedback
TL;DR: Simulations show that close to optimum performance can be attained with only 2 bits per channel dwell time block, even for systems with many transmit antennas, which clears a hurdle for transmit beamforming with many antennas in FDD mode-which was almost impossible with the prior state-of-art.
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Optimal experimental design for nano-particle atom-counting from high-resolution STEM images.
TL;DR: The principles of detection theory are used to quantify the probability of error for atom-counting from high resolution scanning transmission electron microscopy images and it is concluded that scattering cross-sections perform almost equally well as images and perform better than peak intensities.
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Multistatic pseudolinear target motion analysis using hybrid measurements
TL;DR: A bias-compensated PLE is proposed based on an asymptotic bias analysis of the hybrid PLE, which is incorporated into a weighted instrumental variable (WIV) estimator to obtain asymPTotically unbiased estimates of the target motion parameters.
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.