S
Shankar Narasimhan
Researcher at Indian Institute of Technology Madras
Publications - 120
Citations - 3100
Shankar Narasimhan is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Kalman filter & Extended Kalman filter. The author has an hindex of 28, co-authored 119 publications receiving 2780 citations. Previous affiliations of Shankar Narasimhan include Northwestern University & Indian Institute of Technology Kanpur.
Papers
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Generalized likelihood ratio method for gross error identification
TL;DR: In this paper, a nouvelle methode statistique is utilisee for detecter, identifier and estimer les grosses erreurs (erreurs systematiques and fuites) which peuvent se presenter dans les procedes chimiques en regime permanent.
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Robust and reliable estimation via Unscented Recursive Nonlinear Dynamic Data Reconciliation
TL;DR: In this article, the authors combine the merits of the unscented Kalman filter and the recursive nonlinear dynamic data reconciliation (URNDDR) technique to obtain the UnScented Recursive Nonlinear Dynamic Data Reconciliation (URRD) technique, which provides state and parameter estimates that satisfy bounds and other constraints imposed on them.
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Sensor network design for maximizing reliability of linear processes
Yaqoob Ali,Shankar Narasimhan +1 more
TL;DR: In this paper, the problem of selecting the variables to be measured in order to maximize process reliability was tackled in previous articles (Ali and Narasimhan, 1993) and extended to the optimal design of sensor networks for bilinear processes.
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Nonlinear Bayesian state estimation: A review of recent developments
TL;DR: Various recent developments in the area of nonlinear state estimators from a Bayesian perspective are reviewed, including the constrained state estimation, the handling of multi-rate and delayed measurements and recent advances in model parameter estimation.
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Incorporating delayed and infrequent measurements in Extended Kalman Filter based nonlinear state estimation
TL;DR: This paper analyzes several existing methods to incorporate measurement delays and reinterpret their results under a common unified framework (for Extended Kalman Filter) and presents extensions to handle time-varying and uncertain delays, as well as out of sequence measurement arrival.