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.

Resonator-based filter-banks for frequency-domain applications

Abstract: The filter-bank structure proposed is based on a digital simulation of a singly terminated ladder filter. This filter bank can also be arrived at from a filter described by G Peceli (1989) and represents an extremely hardware efficient structure, having a complexity of O(N). The main application examined is adaptive line enhancement. The filter-bank-based line enhancer is shown to have the necessary conditions for global convergence and to yield uncorrelated sinusoidal enhanced outputs that are undistorted versions of the corresponding frequency components of the input. A number of additional possible applications for the filter-bank are described. These include the tracking of periodic signals, subband coding, frequency-domain adaptive noise-cancellation, and frequency-domain processing of signals from phased arrays. >

Training analysis of adaptive interference suppression for direct-sequence code-division multiple-access systems

Abstract: This paper studies the transient behavior of an adaptive near-far resistant receiver for direct-sequence (DS) code-division multiple-access (CDMA) known as the minimum mean-squared error (MMSE) receiver. This receiver structure is known to be near-far resistant and yet does not require the large amounts of side information that are typically required for other near-far resistant receivers. In fact, this receiver only requires code timing on the one desired signal. The MMSE receiver uses an adaptive filter which is operated in a manner similar to adaptive equalizers. Initially there is a training period where the filter locks onto the signal that is sending a known training sequence. After training, the system can then switch to a decision-directed mode and send actual data. This work examines the length of the training period needed as a function of the number of interfering users and the severity of the near-far problem. A standard least mean-square (LMS) algorithm is used to adapt the filter and so the trade-off between convergence and excess mean-squared error is studied. It is found that in almost all cases a step size near 1.0/(total input power) gives the best speed of convergence with a reasonable excess mean-squared error. Also, it is shown that the MMSE receiver can tolerate a 30-40 dB near-far problem without excessively long convergence time.

On adaptive minimum probability of error linear filter receivers for DS-CDMA channels

Abstract: Receiver architectures in the form of a linear filter front-end followed by a hard-limiting decision maker are considered for DS-CDMA communication systems. Based on stochastic approximation concepts a recursive algorithm is developed for the adaptive optimization of the linear filter front-end in the minimum BER sense. The recursive form is decision driven and distribution free. For additive white Gaussian noise (AWGN) channels, theoretical analysis of the BER surface of linear filter receivers identifies the subset of the linear filter space where the optimal receiver lies and offers a formal proof of guaranteed global optimization with probability one for the two-user case. To the extent that the output of a linear DS-CDMA filter can be approximated by a Gaussian random variable, a minimum-mean-square-error optimized linear filter approximates the minimum BER solution. Numerical and simulation results indicate that for realistic AWGN DS-CDMA systems with reasonably low signature cross-correlations the linear minimum BER filter and the MMSE filter exhibit approximately the same performance. The linear minimum BER receiver is superior, however, when either the signature cross-correlation is high or the background noise is non-Gaussian.

Fast least squares adaptive notch filtering

Abstract: An adaptive notch filter based on a fast recursive-least-squares (FLS) algorithm is introduced. The structure is canonical and consists of a transversal section cascaded with an IIR section. The adaptive procedure takes place in the transversal section, and the impact of the notch factor is pointed out. Overall, the FLS notch filter has about the same complexity as the fast transversal adaptive filter, but it performs better for the analysis of narrowband signals in high-level noise. >

Adaptive noise removal from biomedical signals using warped polynomials

Abstract: Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs).