Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Marco Guerriero"'
Autor:
Marco Guerriero, David Haldimann, Marta Sabbadin, Gregorio Ciarlo, Nunzio Bonavita, Yannick Maret
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:1093-1106
The problem of detecting and identifying sensor faults is critical for efficient, safe, regulatory-compliant, and sustainable operations of modern industrial processing systems. The increasing complexity of such systems brings, however, new challenge
Autor:
Gabriel M. Lipsa, Marco Guerriero
Publikováno v:
IEEE Signal Processing Letters. 23:1835-1838
It was shown in [J. R. Hoffman and R. P. Mahler, “Multitarget miss distance via optimal assignment,” IEEE Trans. Syst., Man, Cybern. A, Syst. Humans , vol. 34, no. 3, pp. 327–336, May 2004.] that the Wasserstein distance is equivalent to the me
Publikováno v:
FUSION
The acronym PEMS stands for Predictive Emission Monitoring Systems and designates software analyzers able to provide a reliable and real-time estimate of emission concentrations by means of a data-driven model using real process measurements as input
Publikováno v:
Sustainability in Environment. 3:329
Modeling technologies can provide strong support to existing emission management systems, by means of what is known as a Predictive Emission Monitoring System (PEMS). These systems do not measure emissions through any hardware device, but use compute
Publikováno v:
IEEE Transactions on Signal Processing. 59:4677-4691
In this article, we show that when targets are closely spaced, traditional tracking algorithms can be adjusted to perform better under a performance measure that disregards identity. More specifically, we propose an adjusted version of the joint prob
Publikováno v:
IEEE Transactions on Signal Processing. 58:4311-4321
In distributed multisensor estimation/tracking the problem of fusion is complicated by that of data association (i.e., with false alarms and missed detections): not only is it of concern to provide an estimation-efficient sensor level quantization of
Publikováno v:
IEEE Transactions on Signal Processing. 58:3426-3435
In this paper we introduce a randomly truncated sequential hypothesis test. Using the framework of a repeated significance test (RST), we study a sequential test with truncation time based on a random stopping time. Using the functional central limit
Publikováno v:
IEEE Transactions on Signal Processing. 58:3417-3421
In this correspondence, we study different approaches for Bayesian data fusion for distributed target detection in sensor networks. Due to communication and bandwidth constraints, we assume that each sensor can only transmit a local decision to the f
Publikováno v:
IEEE Transactions on Signal Processing. 57:2629-2639
We introduce a sequential procedure to detect a target with distributed sensors in a two dimensional region. The detection is carried out in a mobile fusion center which successively counts the number of binary decisions reported by local sensors lyi
Publikováno v:
ACSSC
This paper defines and illustrates a tractable methodology for enhanced multiple-hypothesis tracking (MHT) that accounts for undetected target births. The hypothesis space of interest is of the same complexity as in classical MHT. Additionally, the s