Journal ArticleDOI
Broadband ML estimation under model order uncertainty
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TLDR
The max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing, and a novelty of this work is the reduction of indistinguishable components caused by overparameterization.About:
This article is published in Signal Processing.The article was published on 2010-05-01. It has received 8 citations till now. The article focuses on the topics: Statistical hypothesis testing & Array processing.read more
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Book ChapterDOI
DOA Estimation Methods and Algorithms
TL;DR: This article provides an overview of DOA estimation methods that are relevant in theory and practice and presents estimators based on beamforming, subspace and parametric approaches and compares their performance in terms of estimation accuracy, resolution capability and computational complexity.
Journal ArticleDOI
Improving time series modeling by decomposing and analyzing stochastic and deterministic influences
TL;DR: The proposed approach considers the Empirical Mode Decomposition method and a Recurrence Plot-based measurement to decompose and assess stochastic and deterministic influences to improve time series modeling.
Journal ArticleDOI
DOA estimation of wideband sources without estimating the number of sources
TL;DR: A new technique to estimate wideband source directions from the sensor snapshots without requiring to know the number of sources present in the scenario by introducing a new data dependent term into the optimization problem, thus achieving wideband capability.
Journal ArticleDOI
Derivative-constrained frequency-domain wideband DOA estimation
TL;DR: A new approach for wideband direction of arrival (DOA) estimation incorporating derivative constraints into the optimization problem is presented incorporating derivatives of the array manifold matrix into the optimized problem.
References
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Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Book
Continuous univariate distributions
TL;DR: Continuous Distributions (General) Normal Distributions Lognormal Distributions Inverse Gaussian (Wald) Distributions Cauchy Distribution Gamma Distributions Chi-Square Distributions Including Chi and Rayleigh Exponential Distributions Pareto Distributions Weibull Distributions Abbreviations Indexes
Journal ArticleDOI
Maximum likelihood estimation of misspecified models
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.
Book
Time Series: Data Analysis and Theory
TL;DR: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.
Book
The Bootstrap and Edgeworth Expansion
TL;DR: In this paper, the authors present a non-Edgeworth view of the Bootstrap and propose a method of importance sampling for estimating bias, variance, and skewness.