Adaptive Filter Theory

By S. Haykin. 1986

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Citations in the corpus

Robust Transmural Electrophysiological Imaging: Integrating Sparse and Dynamic Physiological Models into ECG-Based Inference (2015, background): “In dynamic inference, a common solution is to augment the unknown system state with auxiliary variables representing unknown model errors [4].”
Hierarchical Mixtures of Experts and the EM Algorithm (1993, methodology): “Any of the fast (cid:12)lter algorithms (Haykin, 1991) could also be utilized, giving rise to a family of on-line algorithms.”
Real-time multiple vehicle detection and tracking from a moving vehicle (2000, background): “Road boundaries and lane markings are detected by a spatial recursive least squares filter (RLS) [33] with the main goal of guiding the search for vehicles.”
Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, background): “In this context, the signals are made as much distinguishable as possible in one domain, and as much overlapping as possible in the remaining domains.”
Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering (2004, methodology): “To conduct this analysis, we augmented the state-space of the SSPPF to accommodate all the receptive field parameter estimates as well as the hand velocity signal (Ljung & Soderstrom, 1987; Haykin, 1996).”
On channel estimation and optimal training design for amplify and forward relay networks (2008, background): “Since (28) is in quadratic form, it is sufficient to consider only ∂L(C, μi)/∂C [22].”
Support vector channel selection in BCI (2004, unknown): “prediction [5].”
MIMO radar waveform design based on mutual information and minimum mean-square error estimation (2007, methodology): “The scaled form of Φ is referred to as the sample correlation matrix [25].”
How many people are able to operate an EEG-based brain-computer interface (BCI)? (2003, methodology): “AAR parameters were estimated with the recursive-least-squares (RLS) algorithm [11], [12].”