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Adaptive Constrained Learning in Reproducing Kernel Hilbert Spaces: The Robust Beamforming Case

This paper establishes a new paradigm for convexly constrained adaptive learning in reproducing kernel Hilbert spaces (RKHS). Although the technique is of a general nature, we present it in the context of the beamforming problem. A priori knowledge, like beampattern specifications and constraints concerning robustness against steering vector errors, takes the form of multiple closed convex sets in a high (possibly infinite) dimensional RKHS. Every robustness constraint is shown to be equivalent to a min-max optimization task formed by means of the robust statistics epsiv -insensitive loss function. Such a multiplicity of specifications turns out to obtain a simple expression by using the rich frame of fixed-point sets of certain mappings defined in a Hilbert space. Moreover, the cost function, that the final solution has to optimize, is expressed as an infinite sequence of convex, nondifferentiable loss functions, springing from the sequence of the incoming training data. A novel adaptive beamforming design, of linear complexity with respect to the number of unknown parameters, to such a constrained nonlinear learning problem is derived by employing a very recently developed version of the adaptive projected subgradient method (APSM). The method produces a sequence that, under mild conditions, exhibits properties like the strong convergence to a beamformer that satisfies all of the imposed constraints, and in the meantime asymptotically minimizes the sequence of the loss functions imposed by the training data. The numerical examples demonstrate that the proposed method displays increased resolution in cases where the classical linear beamforming solutions collapse. Moreover, it leads to solutions, which are in agreement with the imposed a priori knowledge, as opposed to unconstrained online kernel regression techniques.