A general framework for adaptive and non-adaptive spacetime beampattern synthesis using quadratic beampattern constraints with minimum mean-square error (MMSE) and linearly constrained minimum variance (LCMV) beamforming is presented. Main beam and sidelobe pattern control is achieved by imposing a set of inequality constraints on the weighted mean-square error between the adaptive pattern and ...

Abstract: Rejection of coherent interference requires an array consisting of several identical sub-arrays which operates with spatial smoothing algorithms. For the cases in which such array configuration cannot be obtained, other schemes must be employed to decouple the desired signal and the coherent interference. This paper presents a coherent interference suppression scheme, known as, the co...

This paper presents a tutorial-like detailed explanation of linearly constrained minimum-variance filtering in order to introduce an efficient implementation that utilizes Householder transformation (HT). Through a graphical description of the algorithms, further insight on linearly constrained adaptive filters was made possible, and the main differences among several algorithms were highlighte...

We propose a control scheme for a discrete-time linear stochastic multi-input multi-output (MIMO) system, with imperfectly known or slowly time-varying parameters. The selected approach is generalized minimum variance control, but the method can easily be extended to generalized predictive control. The quadratic criterion to be minimized penalizes the variance of the difference between the outp...

In this paper, the design and real time implementation of a Nonlinear Minimum Variance (NMV) estimator is presented using a laboratory based ball and beam system. The real time implementation employs a LabVIEW based tool. The novelty of this work lies in the design steps and the practical implementation of the NMV estimation technique which up till now only investigated using simulation studies...

Chi and Mendel [I], [9] analyzed the performance of the minimum-variance deconvolution (MVD). In this correspondence, a further analysis of the performance of the MVD filter is presented. We show that the MVD filter performs like an inverse filter and a whitening filter as SNR + 00, and like a matched filter as SNR + 0. The estimation error e (k) of the MVD filter is colored noise, but it becom...