Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

Extended least squares and their applications to adaptive control and prediction in linear systems

Abstract: Herein strong consistency of recursive extended least squares is established under considerably weaker assumptions than previously assumed in the literature. The argument used to establish consistency also leads to certain basic properties of adaptive predictors based on these recursive estimators. Making use of these properties of the adaptive predictors, simple modifications of the Astrom-Wittenmark self-tuning regulator are proposed and shown to be asymptotically optimal.

Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images

Abstract: A novel adaptive median filter is proposed. It allows the simultaneous removal of a combination of signal-dependent and additive random noise in addition to mixed impulse noise in images, processed in a single filtering pass. The adaptation algorithm is based on the local signal-to-noise ratio. An extension of the class of nonlinear mean filters to adaptive filters is considered. The performance of the adaptive median filter is compared to the commonly used median filter and the nonlinear mean filter.

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

Abstract: In this paper, we propose a reduced-rank space-time adaptive processing (STAP) technique for airborne phased array radar applications. The proposed STAP method performs dimensionality reduction by using a reduced-rank switched joint interpolation, decimation and filtering algorithm (RR-SJIDF). In this scheme, a multiple-processing-branch (MPB) framework, which contains a set of jointly optimized interpolation, decimation and filtering units, is proposed to adaptively process the observations and suppress jammers and clutter. The output is switched to the branch with the best performance according to the minimum variance criterion. In order to design the decimation unit, we present an optimal decimation scheme and a low-complexity decimation scheme. We also develop two adaptive implementations for the proposed scheme, one based on a recursive least squares (RLS) algorithm and the other on a constrained conjugate gradient (CCG) algorithm. The proposed adaptive algorithms are tested with simulated radar data. The simulation results show that the proposed RR-SJIDF STAP schemes with both the RLS and the CCG algorithms converge at a very fast speed and provide a considerable SINR improvement over the state-of-the-art reduced-rank schemes.

Least mean M-estimate algorithms for robust adaptive filtering in impulse noise

Abstract: This paper proposes two gradient-based adaptive algorithms, called the least mean M estimate and the transform domain least mean M-estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M-estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method fur estimating the required thresholds in the M-estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses.