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.

Echo canceller with means for determining filter coefficients from autocorrelation and cross-correlation coefficients

Abstract: In a data transmission system where a first signal partially leaks as an echo from a first transmission line to a second transmission line through a hybrid circuit to form a mixed signal of the echo and a second signal on the second transmission line, an echo canceller is used for cancelling the echo by producing an echo replica at a transversal filter according to filter coefficients and subtracting the echo replica from the mixed signal. In order to reliably generate the filter coefficient for a reduced time duration, a series of autocorrelation coefficients of the first signal and a series of cross-correlation coefficients between the first and the mixed signals are calculated at calculators and the filter coefficients are determined from both of the autocorrelation and the cross-correlation coefficient series at a coefficient determining circuit. The coefficient determining circuit may be an arithmetic circuit for solving simultaneous linear equations. Another circuit for determining the filter coefficients may be a circuit where a first filter coefficient is determined by detecting the maximum value and the corresponding delay time from the series of cross-correlation coefficients, making a fresh series of cross-correlation coefficients with reference to the maximum values, the delay time and the first filter coefficient, then, determining a second filter coefficient from the fresh series. Then, filter coefficients are determined by repetition of the similar operation.

Adaptive packet selection for clock recovery

Abstract: Packet delay variation (PDV) is a dominant source of noise in packet-based synchronization systems. To filter this type of noise, many clock recovery algorithms select packets based on the sample-minimum statistic of the network transit time. Although such a filter can be very effective in certain types of networks, there are just as many networks and background traffic patterns for which sample-minimum is far from optimal. In this paper, we propose a filter that dynamically evaluates multiple packet selection criteria and selects the one that currently minimizes the noise. We also present the results of an experimental evaluation of the new adaptive filter.

Modified recursive maximum likelihood adaptive filter for nonlinear aircraft flight-path reconstruction

Abstract: Nonlinear aircraft flight-path reconstruction is basically a state estimation problem that can be solved with adaptive filtering techniques, as it involves the estimation of flight trajectories and unknown parameters such as biases, scale factors, and noise statistical uncertainties of flight instrumentation systems. Among many algorithms, the recursive maximum likelihood (RML) method is a popular scheme in adaptive filtering for nonlinear state-parameter estimation problems. However, the RML algorithm is sensitive to initialization errors of system parameters. Divergence may occur at large values of these errors. The objective of the present study is to develop a modified recursive maximum likelihood (MRML) adaptive filter that is less sensitive to the effects of initialization errors. The new algorithm revises the conventional RML adaptive filter by including the effect of the parameter estimator in the prediction error vector computation. Numerical results are presented for a nonlinear aircraft model. System states and a variety of parameters including measurement noise standard deviations were estimated with the conventional RML adaptive filter and compared with corresponding estimates of the new MRML adaptive filter. Numerical simulations were carried out with different a priori estimates of parameters and system state vector elements. The results indicate that the MRML adaptive filter, as developed here, produces estimates that are both more accurate and less sensitive to parameter initialization errors than those obtained with the conventional RML adaptive filter.

Adaptive-Differential-Evolution-Based Design of Two-Channel Quadrature Mirror Filter Banks for Sub-Band Coding and Data Transmission

Abstract: This paper proposes an improved and adaptive variant of the differential evolution algorithm for the design of two-channel quadrature mirror filters with linear phase characteristics. To match the ideal system response characteristics, the algorithm is employed to optimize the values of the filter bank coefficients. The filter response is optimized in both passband and stopband. The overall filter bank response aims at minimizing objectives like reconstruction error, mean square error in passband, and mean square error in stopband. Effective designing can be achieved by efficiently minimizing the objective function. The proposed algorithm is able to perform better than the other existing design methods. Five different design examples are presented to validate the effectiveness of the proposed approach over other conventional design techniques, as well as state-of-the-art evolutionary algorithms found in the literature.