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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
Detecting multiple change-points in the mean of Gaussian process by model selection
TL;DR: This paper proposes to estimate change-points in the mean of a signal corrupted by an additive Gaussian noise with a method based on a penalized least-squares criterion, and chooses the penalty function such that the resulting estimator minimizes the quadratic risk.
Journal ArticleDOI
Coarrays, MUSIC, and the Cramér–Rao Bound
Mianzhi Wang,Arye Nehorai +1 more
TL;DR: This paper derives a simplified asymptotic mean square error (MSE) expression for the MUSIC algorithm applied to the coarray model, which is applicable even if the source number exceeds the sensor number, and shows that when there are more sources than the number of sensors, the MSE converges to a positive value instead of zero when the signal-to-noise ratio (SNR) goes to infinity.
Journal ArticleDOI
Capacity maximizing MMSE-optimal pilots for wireless OFDM over frequency-selective block Rayleigh-fading channels
TL;DR: Considering orthogonal frequency-division multiplexing systems with decoupled information-bearing symbols from pilot symbols transmitted over wireless frequency-selective Rayleigh-fading channels, it is shown that equispaced and equipowered pilot symbols are optimal in terms of minimizing the mean-square channel estimation error.
Journal ArticleDOI
Decentralized estimation in an inhomogeneous sensing environment
Jin-Jun Xiao,Zhi-Quan Luo +1 more
TL;DR: A decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signal-to-noise ratio (SNR) and the MSE is within a constant factor of 25/8 of that achieved by the classical centralized BLUE estimator.
Reference BookDOI
Speech processing : a dynamic and optimization-oriented approach
Li Deng,Douglas O'Shaughnessy +1 more
TL;DR: Analysis of discrete-time speech signals probability and random processes linear model and dynamic system model optimization methods and estimation theory statistical pattern recognition helps clarify speech technology in selected areas.
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.