Kalman filter

Known as: Kalman-Bucy filter, The Kalman Smoother, Stratonovich-Kalman-Bucy 
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing… (More)
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Topic mentions per year

Topic mentions per year

1966-2017
01000200019662017

Papers overview

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Highly Cited
2009
Highly Cited
2009
In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the <i>cubature</i… (More)
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Highly Cited
2007
Highly Cited
2007
In this paper, we introduce three novel distributed Kalman filtering (DKF) algorithms for sensor networks. The first algorithm is… (More)
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Highly Cited
2005
Highly Cited
2005
The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation… (More)
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Highly Cited
2004
Highly Cited
2004
Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering… (More)
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Highly Cited
2002
Highly Cited
2002
Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley… (More)
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Highly Cited
2001
Highly Cited
2001
In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear… (More)
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Highly Cited
2000
Highly Cited
2000
The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning… (More)
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Highly Cited
1998
Highly Cited
1998
This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the ensemble… (More)
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Highly Cited
1997
Highly Cited
1997
The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality… (More)
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Highly Cited
1994
Highly Cited
1994
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem… (More)
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