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.

Adaptive switched-capacitor filters based on the LMS algorithm

Abstract: The implementation of adaptive FIR filters based on the LMS algorithm using SC circuits is described. Basically, the filters consist of a delay element, a summing circuit, an integrator, and a multiplier. The influence of nonideal effects of SC networks on the behavior of a given filter is investigated. It is shown that the nonidealities in the FIR filter part of the circuit can be eliminated by an additional constant tap element, whereas the main error source in the adaptation part is the multiplier- and integrator offset-errors. The errors can be compensated for using special offset-free circuits. Using the proposed offset-compensation schemes, the accuracy of a switched-capacitor adaptive filter is mainly determined by the nonlinearity errors of the multipliers. >

Comparison of several ballistic target tracking filters

Abstract: In this paper, we compare several nonlinear filtering methods, namely, extended Kalman filter (EKF), unscented filter (UF), particle filter (PF), and linear minimum mean square error (LMMSE) filter for a ballistic target tracking problem. We cast EKF and UF into a general linear recursive estimation framework and reveal their pros and cons. We pinpoint using the LMMSE filter for possible analytical solutions rather than starting with approximations such as system linearization or unscented transform. We compare the performance of EKF, UF, LMMSE filter and Gaussian PF for a ballistic target tracking problem. The estimation accuracy is also compared with the posterior Cramer-Rao lower bound (PCRLB). Our simulation results confirm that the LMMSE filter outperforms EKF and UF in terms of tracking accuracy, filter credibility and robustness against the sensitivity to filter initial condition. Its accuracy is slightly worse than that of Gaussian PF but with much lower computational load. We conclude that the LMMSE filter is preferred for the ballistic target tracking problem being studied.

Prototype Filter Design Approaches for Near Perfect Reconstruction Cosine Modulated Filter Banks - A Review

Abstract: Cosine Modulated Filter Banks (CMFB) are very popular among the different maximally decimated filter banks due to their design ease and simplicity in implementation and the property that all the coefficients of all the filters are real. All the analysis and synthesis filters are derived from one or two prototype filters. Hence, recently, the design of the prototype filter in a CMFB has become a subject of interest in the field of multirate signal processing. Perfect Reconstruction (PR) filter banks are those which can produce at the output, a weighted delayed version of the input. But in most of the applications a near perfect reconstruction (NPR) is sufficient. This can reduce the computational complexity. Different approaches developed for the efficient and optimal design of the prototype filter in a NPR orthogonal CMFB are studied, classified and summarized in this paper. In today's applications, less space and low power consumption are very essential. When the different design approaches for the design of the prototype filter in CMFB are compared, it is observed that the one using frequency response masking(FRM) and meta-heuristic optimization techniques gives better performance in terms of implementation complexity, which in turn can lead to reduced chip size and power consumption. It is hoped that this review will be highly beneficial to the researchers working in the area of multirate signal processing. At the end, we also propose some novel design approaches for the design of low complexity prototype filter using FRM technique.

Super-resolution restoration of continuous image sequence - adaptive filtering approach

Abstract: In this paper, we propose computationally efficient super-resolution restoration algorithms for blurred, noisy and down-sampled continuous image sequences. The proposed approach is a generalization of the stochastic estimation based methods (the ML and the MAP estimators) for the restoration of single blurred and noisy images. The blur, decimation, and noise degredations are modeled as a sparse matrices linear equation connecting the measurements to the ideal required image. A second linear equation can be added in order to include a localy adaptive spatial smoothness prior, similar to the way it is done in the Constrained Least Squares method. Based on these two equations, a classic super-resolution restoration approach can be suggested as a by-product of the above formulation. This way, a single improved resolution image can be restored from several warped, blurred, noisy and down-sampled versions of it. The adaptive spatial regularization term is shown to improve the restored image sequence quality by forcing smoothness, while preserving edges. When attempting to treat continuous image sequences, the temporal axis is to included into the model. This is done by using temporal smoothness assumption along motion trajectories, resulting with a calssic state-space equations model. The obtained model can serve as a basis for the application of the Kalman filter (KF), but a direct application of the KF is far to complex to imlement. The state-space equations model are thus further simplified, yielding a Least Squares (LS) model an instantaneous squared error quality measure which is to be minimized over time. The RLS and LMS adaptive algorithms can be applied directly to the new simplified model. This way, simple yet very effective two recursive algorithms for the estimation of the restored improved resolution image sequence in time are composed. The computation complexity of the obtained algorithms is of the order O L L { log } 2 ( ) per one output image, where L2 is the number of pixels in the output image. Simulations carried out on test sequences prove these methods to be applicable, efficient, and with very promising results.

Adaptive time delay estimation with constraints

Abstract: The time (shift) delay parameter between two signals is modeled as a finite-impulse response filter whose coefficients are samples of a sinc function. The time-domain LMS (least-mean-squares) adaptive algorithm is used, but only the weight with the largest magnitude is updated, which involves less computation. The result is a faster adaptation and the elimination of interpolation needed in previous approaches to obtain nonintegral (multiples of sampling period) time-delay estimates. >