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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TL;DR: This work presents data-adaptive methodologies for selecting the training data of adaptive array algorithms based on sample matrix inversion and describes these algorithms and their performance on recorded radar data.
Abstract: Adaptive array algorithms based on sample matrix inversion (SMI) require the availability of a secondary data set to "train" the adaptive filter. Numerous data-independent rules have been proposed for selecting this training data. However, such rules often perform poorly in inhomogeneous environments. We present data-adaptive methodologies for selecting the training data. The techniques, called "Power Selected Training" and "Power Selected Deemphasis", use measurements of the interference environment to select training data. This work describes these algorithms and their performance on recorded radar data.
142 citations
14 Apr 1991
TL;DR: The proposed image contrast enhancement technique is based on combining the original image with its filtered version obtained using one of the two nonlinear filters.
Abstract: Two types of very simple two-dimensional nonlinear filters are introduced and applied to image contrast enhancement. The first type is based on a generalization of the Teager's algorithm. A theoretical analysis has shown that this type of nonlinear filter works like a local-mean-weighted highpass filter. Based on this analysis, a second type of nonlinear filter has been developed which works like local-mean-weighted bandpass filter. The proposed image contrast enhancement technique is based on combining the original image with its filtered version obtained using one of the two nonlinear filters. Very high quality enhancement has been achieved for natural images. >
142 citations
TL;DR: This paper proposes and analyze nonlinear least squares methods which process the data incrementally, one data block at a time, and focuses on the extended Kalman filter, which may be viewed as an incremental version of the Gauss--Newton method.
Abstract: In this paper we propose and analyze nonlinear least squares methods which process the data incrementally, one data block at a time. Such methods are well suited for large data sets and real time operation and have received much attention in the context of neural network training problems. We focus on the extended Kalman filter, which may be viewed as an incremental version of the Gauss--Newton method. We provide a nonstochastic analysis of its convergence properties, and we discuss variants aimed at accelerating its convergence.
141 citations
TL;DR: An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated and some application examples are presented, finding that when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred.
Abstract: An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated. First, some useful properties of MPE function are studied. Three main results are as follows: 1) MPE function is a convex function of filter coefficients; so it has no local minima. 2) When input process and desired process are both Gaussian processes, then MPE function has the same optimum solution as the conventional Wiener solution for any p. 3) When input process and desired process are non-Gaussian processes, then MPE function may have better optimum solution than Wiener solution. Next, a least mean p-power (LMP) error adaptive algorithm is derived and some application examples are presented. Consequently, when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred. Furthermore, when the signal is corrupted by noise or interference, the adaptive algorithm with proper choice of p may be preferred. >
141 citations
TL;DR: Simulations demonstrate that the proposed nonlinear filter is effective as a method for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR).
Abstract: A nonlinear filter is proposed for estimating a complex sinusoidal signal and its parameters (frequency, amplitude, and phase) from measurements corrupted by white noise. This filter is derived by applying an extended complex Kalman filter (ECKF) to a nonlinear stochastic system whose state variables are a function of its frequency and a sample of an original signal, and then, proof of the stability is given in the case of a single complex sinusoid. Simulations demonstrate that the proposed nonlinear filter is effective as a method for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR). In addition, the effect of the initial condition in the filter on frequency estimation is also discussed.
140 citations