Zobrazeno 1 - 10
of 1 323
pro vyhledávání: '"Routtenberg, A."'
Autor:
Morgenstern, Gal, Routtenberg, Tirza
This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a diffused sparse graph signal, is seminal in the field of gr
Externí odkaz:
http://arxiv.org/abs/2405.10649
Autor:
Gabay, Yaela, Shlezinger, Nir, Routtenberg, Tirza, Ghasempour, Yasaman, Alexandropoulos, George C., Eldar, Yonina C.
Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communicatio
Externí odkaz:
http://arxiv.org/abs/2312.08833
Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots. This paper presents NUV-DoA algorithm, that augments Bayesian sparse reconstruction with s
Externí odkaz:
http://arxiv.org/abs/2309.03114
Autor:
Harel, Nadav, Routtenberg, Tirza
In many parameter estimation problems, the exact model is unknown and is assumed to belong to a set of candidate models. In such cases, a predetermined data-based selection rule selects a parametric model from a set of candidates before the parameter
Externí odkaz:
http://arxiv.org/abs/2308.11359
In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems. The proposed approach is based on a constrained maximum likelihood es
Externí odkaz:
http://arxiv.org/abs/2308.03392
Autor:
Dabush, Lital, Routtenberg, Tirza
Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph. A crucial prerequisite for applying various GSP and graph neural netwo
Externí odkaz:
http://arxiv.org/abs/2305.19618
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. Recent research has focused on developing GSP-based methods for state estimation, attack detection, and topology ident
Externí odkaz:
http://arxiv.org/abs/2304.10801
In constrained parameter estimation, the classical constrained Cramer-Rao bound (CCRB) and the recent Lehmann-unbiased CCRB (LU-CCRB) are lower bounds on the performance of mean-unbiased and Lehmann-unbiased estimators, respectively. Both the CCRB an
Externí odkaz:
http://arxiv.org/abs/2304.07942
Autor:
Buchnik, Itay, Steger, Damiano, Revach, Guy, van Sloun, Ruud J. G., Routtenberg, Tirza, Shlezinger, Nir
The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applicability under complex settings, e.g., when tracking based on visual da
Externí odkaz:
http://arxiv.org/abs/2304.07827
The Laplacian-constrained Gaussian Markov Random Field (LGMRF) is a common multivariate statistical model for learning a weighted sparse dependency graph from given data. This graph learning problem can be formulated as a maximum likelihood estimatio
Externí odkaz:
http://arxiv.org/abs/2302.06434