Topic
Kernel adaptive filter
About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: A new quantizedkernel adaptive filter called quantized kernel maximum correntropy (QKMC) is developed, which is robust to large outliers or impulsive noises, and a sufficient condition for guaranteeing convergence is obtained.
45 citations
TL;DR: The theory of particle flow is generalized to stabilize the nonlinear filter and implements Bayes' rule using particle flow rather than with a pointwise multiplication of two functions, avoiding one of the fundamental and well known problems in particle filters, namely "particle degeneracy".
Abstract: We generalize the theory of particle flow to stabilize the nonlinear filter. We have
invented a new nonlinear filter that is vastly superior to the classic particle filter and the extended
Kalman filter (EKF). In particular, the computational complexity of the new filter is many orders of
magnitude less than the classic particle filter with optimal estimation accuracy for problems with
dimension greater than 4. Our accuracy is typically several orders of magnitude better than the
EKF for nonlinear problems. We do not resample, and we do not use any proposal density from
an EKF or UKF or other filter. Moreover, our new algorithm is deterministic, and we do not use
any MCMC methods; this is a radical departure from other particle filters. The new filter
implements Bayes' rule using particle flow rather than with a pointwise multiplication of two
functions; this avoids one of the fundamental and well known problems in particle filters, namely
"particle degeneracy." In addition, we explicitly stabilize our particle filter using negative
feedback, unlike standard particle filters, which are generally very inaccurate for plants with slow
mixing or unstable dynamics. This stabilization improves performance by several orders of
magnitude for difficult problems.
45 citations
TL;DR: In this paper, a sound processor including a microphone, a pre-amplifier, a bank of N parallel filters, and an N-parallel filter bank is used to detect short-duration transitions in the envelope signal of each filter channel.
Abstract: A sound processor including a microphone (1), a pre-amplifier (2), a bank of N parallel filters (3), means for detecting short-duration transitions in the envelope signal of each filter channel, and means for applying gain to the outputs of these filter channels in which the gain is related to a function of the second-order derivative of the slow-varying envelope signal in each filter channel, to assist in perception of low-intensity short-duration speech features in said signal.
45 citations
TL;DR: In this article, a nonintrusive inverse heat transfer procedure for predicting the time-varying thickness of the protective phase-change ledge on the inside surface of the walls of a high-temperature metallurgical reactor is presented.
44 citations
Patent•
02 Oct 2002
TL;DR: In this article, a method and apparatus for estimation of vehicle forward path geometry utilizing an adaptive Kalman filter bank and a two-clothoid road model is presented, where each of a plurality of Kalman filters, utilizing the latest available measurement vector Y k at time k, estimates the state vector X k and error covariance matrix P k.
Abstract: The present invention provides a method and apparatus for estimation of vehicle forward path geometry utilizing an adaptive Kalman filter bank and a two-clothoid road model. The invention provides that each of a plurality of Kalman filters, utilizing the latest available measurement vector Y k at time k, estimates the state vector X k and error covariance matrix P k . The outputs of filter 504 a , 504 b , and 504 c denoted as as X k j and P k j , are provided to a plurality of weighting elements, which calculate weight factors, W k j 506 a , 506 b , and 506 c for each filter. The weight factor of each filter is the probability that the upcoming road geometry matches the road model hypothesized in the filter. After being assigned a weighted value, the weighted value road models are fused in a fusion element 508 , and a weighted output road geometry model is provided.
44 citations