Výsledky vyhledávání - "YAAKOV BAR-SHALOM"

Akademický článek

ML-PMHT Tracking of Multiple Wideband Sources by Fusing Data From a Distributed Acoustic Vector Sensor Array

Autor: Benjamin P. Brown, Peter K. Willett, Yaakov Bar-Shalom, James H. Miller

Publikováno v: IEEE Access, Vol 12, Pp 101626-101645 (2024)

Acoustic vector sensors provide a measurement of both the scalar acoustic pressure as well as the acoustic particle velocity vector in the orthogonal Cartesian directions, allowing for direction of arrival estimation of an acoustic source. Typically,

Externí odkaz: https://doaj.org/article/74d257b228ff48fb8926e5a1fda5d4b6

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Akademický článek

INS Fine Alignment With Low-Cost Gyroscopes: Adaptive Filters for Different Measurement Types

Autor: Itzik Klein, Yaakov Bar-Shalom

Publikováno v: IEEE Access, Vol 9, Pp 79021-79032 (2021)

Inertial navigation system stationary fine alignment process is a critical step in reducing the initial errors of the attitude and sensor biases. While many studies had been made for tactical grade systems, less attention was given to low-cost sensor

Externí odkaz: https://doaj.org/article/7eb46b667fe947eba528483e0441d183

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Akademický článek

On the Identification of Noise Covariances and Adaptive Kalman Filtering: A New Look at a 50 Year-Old Problem

Autor: Lingyi Zhang, David Sidoti, Adam Bienkowski, Krishna R. Pattipati, Yaakov Bar-Shalom, David L. Kleinman

Publikováno v: IEEE Access, Vol 8, Pp 59362-59388 (2020)

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown. Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions

Externí odkaz: https://doaj.org/article/eb4a58f788ec438f9895e0541d18f839

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Akademický článek

ML-PDA: Advances and a New Multitarget Approach

Autor: Yaakov Bar-Shalom, Peter Willett, Wayne Blanding

Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2008 (2007)

Developed over 15 years ago, the maximum-likelihood-probabilistic data association target tracking algorithm has been demonstrated to be effective in tracking very low observable (VLO) targets where target signal-to-noise ratios (SNRs) require very l

Externí odkaz: https://doaj.org/article/9a6c8e4b860345ea96a6c8ac239fd512

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