Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

By D. Simon. 2010

935 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f411a3eb7d6a93c491bb3ae7972254e5d6b58d7d

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In Aerospace Engineering

In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, background): “This is shown by Simon in [52], where different approaches to solve the issue are reviewed.”
Human motion trajectory prediction: a survey (2019, methodology): “The methods developed to this end differ in how road constraints are derived and incorporated into the state estimation problem, see the survey by Simon (2010).”
Learning Switching Models for Abnormality Detection for Autonomous Driving (2018, methodology): “Several state estimation methods have been employed for modeling linear and nonlinear dynamics [15], [16].”
A Discontinuous Extended Kalman Filter for non-smooth dynamic problems (2017, methodology): “For the case of the EKF this is carried out by linearizing 500 this constraint and imposing it after the measurement-update ([33]):”
On unscented Kalman filtering with state interval constraints (2010, background): “…Ann Arbor, MI, USA a r t i c l e i n f o Article history: Received 20 November 2008 Received in revised form 15 October 2009 Accepted 15 October 2009 Keywords: State estimation Interval constraints Unscented Kalman filter a b s t r a c t This paper addresses the state-estimation problem for…”
AI-IMU Dead-Reckoning (2019, background): “It is then customary to generate a ﬁctitious observation from the constraint function: and to feed the ﬁlter with the information that y n = 0 (pseudo-measurement) as ﬁrst advocated by [29], see also [13,30] for application to visual inertial localization and general considerations.”