Extension of Impulse Detectors to Spatial Dimension and their Utilization as Switch in the LMS L-SD Filter

In this paper, one kind of adaptive LMS filters based on order statistics is used for two-dimensional filtration of noisy greyscale images degraded by mixed noise. The signal-dependent adaptive LMS L-filter (L-SD) consists of two normalized constrained adaptive LMS L-filters, because they have better convergence properties than simple LMS algorithm. Moreover, first filter suppresses the noise in homogeneous regions and second filter preserves the high components of filtered image. Some versions of spatial order statistic detectors were developed from the impulse detectors and were employed as switch between output these filters

Adaptive Mixture Methods Based on Bregman Divergences

We investigate adaptive mixture methods that linearly combine outputs of $m$ constituent filters running in parallel to model a desired signal. We use "Bregman divergences" and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of $m$ constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems.Comment: Submitted to Digital Signal Processing, Elsevier; IEEE.or

Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems

A generalised sidelobe canceller architecture based on oversampled subband decompositions

Adaptive broadband beamforming can be performed in oversampled subband signals, whereby an independent beamformer is operated in each frequency band. This has been shown to result in a considerably reduced computational complexity. In this paper, we primarily investigate the convergence behaviour of the generalised sidelobe canceller (GSC) based on normalised least mean squares algorithm (NLMS) when operated in subbands. The minimum mean squared error can be limited, amongst other factors, by the aliasing present in the subbands. With regard to convergence speed, there is strong indication that the subband-GSC converges faster than a fullband counterpart of similar modelling capabilities. Simulations are presented