Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation

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Sigma-point Kalman filtering for integrated GPS and inertial navigation (2005, methodology): “A sigma-point GPS/INS filter has been presented in [17], which also includes a method to fuse latency lagged observations in a theoretically consistent fashion.”
Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter (2006, unknown): “The standard UKF algorithm can be summarized as follows [24] ) sigma pointsˆX a k = ˆ x a k ˆ x a k − γ ˆ P a k ˆ x a k + γ ˆ P a k (19) 3) Prediction (time update): ˆ χ a k+1 = f d ˆ χ x k , u k , ˆ χ w k (20) ˜ x a k+1 = 2n a i=0 W m i ˆ χ a i,k+1 (21) ˜ P k+1 = 2n a i=0 W c i ˜ X x i,k+1 − ˜ x…”
Sigma-point kalman filters for probabilistic inference in dynamic state-space models (2004, methodology): “1 SPKF based Time Delayed Sensor Fusion Based on discussions with, and a preliminary idea communicated by Julier [98], we implemented a novel SPKF based latency delayed sensor fusion technique [195] that has not yet been published elsewhere.”
In-Car Positioning and Navigation Technologies—A Survey (2009, methodology): “Therefore, in systems of a highly nonlinear nature and non-Gaussian noise sources, more refined nonlinear filtering approaches such as sigma-point filters (unscented Kalman filters), particle filters (sequential Monte Carlo methods), and exact recursive nonlinear filters, which keep the nonlinear st”