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.
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Papers
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TL;DR: In this article, a procedure for feature extraction using adaptive Schur filter for damage detection in rolling element bearings is proposed, which is characterized by an extremely fast start-up performance, excellent convergence behavior, and fast parameter tracking capability.
46 citations
Patent•
23 Jan 2002TL;DR: In this article, a spatio-temporal filter unit (100) is proposed, which integrates spatial and implicit motion-compensated temporal noise reduction in one filter, where no motion vectors are required.
Abstract: Noise reduction is an important feature in consumer television. This is realized by spatial, temporal or spatio-temporal filters. Spatial filters require pixels from within one image, while temporal filters require samples from two or more successive images. The spatio-temporal filter unit (100) according to this invention integrates spatial and implicit motion-compensated temporal noise reduction in one filter. For the motion compensation no motion vectors are required. The spatio-temporal filter unit (100) is provided with a sigma filter (112) comprising one filter kernel (107) designed to operate on the pixels from both a current image and from the output of the spatio-temporal filter unit, being a temporally recursive filtered image. The operation of the spatio-temporal filter unit (100) can be adjusted by varying the thresholds of the sigma filter (112) and the selection of pixels. The adjustments can be controlled by a motion estimator (222), a motion detector (224) and a noise estimator (220).
46 citations
TL;DR: In this article, it is shown that if the perturbation signal is sufficiently small and a reduced-order dimension model is sufficiently excited, then the output and parameter estimates of this adaptive identifier/filter remain bounded.
Abstract: The reduced-order application of Landau's adaptive output error identifier results in a perturbed error system where the perturbation signal is a moving average of the unmodeled portion of the unknown plant output (or desired signal in adaptive filter parlance). It is proven in this paper that if this perturbation signal is sufficiently small and a reduced-order dimension model is sufficiently excited, then the output and parameter estimates of this adaptive identifier/filter remain bounded. The influence of various operating conditions on this quantitatively defined bound are noted. This robustness property is crucial in all real applications, which due to nonlinearities and distributed effects are subject to reduced-order modeling.
46 citations
TL;DR: Based on the particle filter, an adaptive strong tracking particle filter algorithm is proposed in this paper, according to the residual between actual measurement values and predicted measurement values of every moment, adjustment of the forgetting factor and the weakening factor is adaptively conducted.
Abstract: The primary problem of tracking filtering algorithms is the tracking stability and effectiveness of target states. Based on the particle filter, an adaptive strong tracking particle filter algorithm is proposed in this study. According to the residual between actual measurement values and predicted measurement values of every moment, adjustment of the forgetting factor and the weakening factor is adaptively conducted. Then, by calculating the fading factor, transfer covariance matrix and filter gain of the system are obtained to estimate the particles state value. Updating the importance density function can alleviate the degradation phenomenon of particle filter, and it contributes to effective estimation for the optimal state value of a target. The simulation results demonstrate that the proposed algorithm provides a better tracking precision. In addition, when the target states make mutations, the proposed algorithm can track the mutation states of moving targets effectively and improve the stability of the system.
46 citations
TL;DR: In this paper, an arrangement for converting an electric signal into an acoustic or a mechanic signal comprising a transducer (11), a linear or nonlinear filter (1) with controllable parameters, a sensor (12), a controller (24), a reference filter (20), and a summer (17).
Abstract: An arrangement is provided for converting an electric signal into an acoustic or a mechanic signal comprising a transducer (11), a linear or nonlinear filter (1) with controllable parameters, a sensor (12), a controller (24), a reference filter (20) and a summer (17). The filter (1) is connected to the electric input of the transducer and is adaptively adjusted to compensate for the linear and/or nonlinear distortions of the transducer and to realize a desired overall transfer characteristic. The filter has for every controllable filter parameter an additional output (7) supplying a gradient signal to the controller and a control input (10). The summer (17) provides an error signal derived from the sensor output and reference filter output. The controller contains a circuit (53) for filtering the gradient signal and/or a circuit (25) for filtering the error signal, a multiplier (51) and an integrator (57) for producing a control signal to update every filter parameter. This arrangement omits off-line pre-training and adapts on-line for changing transducer characteristics caused by temperature, ageing and so on.
46 citations