Introduction to random signals and applied kalman filtering (3rd ed

By Richard A. Brown, Phil Hwang. 2012

3,594 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b3b0dcfa63e3bc5a932069c272b518acdf06fb57

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Citations in the corpus

Outlier Analysis (2013, methodology): “Much of this work such as Kalman Filtering [82] has focussed on the removal of noise from and smoothing of temporal time series in order to facilitate more accurate regression and prediction.”
Detailed Real-Time Urban 3D Reconstruction from Video (2007, background): “The feature-to-feature covariances are therefore zero and the influence of their measurements can be incorporated into the filter’s state estimate sequentially rather than as a block [10].”
Secure and resilient distributed machine learning under adversarial environments (2015, methodology): “However, wellknown algorithms such as Kalman filtering [1], SVMs [2] and PCAs [3], are generally vulnerable to adversaries who can modify and generate data to deceive the system to misclassify or misestimate the information from the distributed data processing.”
Adaptive Kalman Filtering for Low-cost INS/GPS (2002, methodology): “Multiple Model Adaptive Estimation (MMAE) uses multiple Kalman ﬁlters that run simultaneously, in this case each using diﬀerent stochastic properties (Brown and Hwang, 1997).”
Ambulatory Position and Orientation Tracking Fusing Magnetic and Inertial Sensing (2007, methodology): “Fusing an inertial navigation system as described above, with other systems is well established in traditional navigation applications [5].”
A Neural Approach to Blind Motion Deblurring (2016, methodology): “One interpretation for these values can be derived from Wiener deconvolution [17], in which ideal restoration is achieved by applying a filter using (2) with coefficients given by”
Pedestrian localisation for indoor environments (2008, methodology): “This is a necessary condition to prevent filter instabilities and likely divergence of the system away from the true state [53].”
Improving Adaptive Kalman Estimation in GPS/INS Integration (2007, methodology): “The operation of the KF relies on the proper deﬁnition of the dynamic and the stochastic models (Brown and Hwang, 1997).”
Nonlinear Complementary Filters on the Special Orthogonal Group (2008, methodology): “This type of filter is also known as distorsionless filtering since the signal is not distorted by the filter [ 42 ].”
Active control of turbulent boundary layers (2003, methodology): “In this case a Kalman filter ( Brown & Hwang 1997 ) could be constructed, as was demonstrated in a low-order numerical simulation by Podvin & Lumley (1998).”
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, background): “Since they are modelled as Gaussian noise, we need to specify the possibly time-variant covariance matrices Rk and Qk [57,69].”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “It was originally proposed in 1960 by Kalman [1960] and it has been extensively studied since then [Luo and Kay 1992; Jacobs 1993; Brown and Hwang 1996].”