A convex combination of H<inf>2</inf> and H<inf>&#x221E;</inf> filters for space-time adaptive equalization

Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification

“…If the convergence of the slower component filters is required to be sped up by transferring a part of the equivalent reflection coefficients to the reflection coefficients of the component filters, the complexities of the proposed schemes increase due to the transfer term in Eq. (34), and momentum terms in Eqs. (33) and (52), which together amount to an additional complexity of (2MN + 5M − N + 3).…”

“…The recent examples of adaptive filter combination tasks include the combination of adaptive filters from different families such as one gradient and one Hessian based in [31], the adaptive combination of proportionate filters for sparse echo cancelation in [32], the adaptive combination of subband adaptive filters for acoustic echo cancelation in [33], the convex combination of H 2 and H ∞ filters for space-time adaptive equalization in [34], the online tracking of the changes in the nonlinearity within a signal by using a collaborative adaptive signal processing approach based on a combination (hybrid) filter in [35], the adaptive combination of Volterra kernels and its application to nonlinear echo cancelation in [36], the convex combination of nonlinear adaptive filters for active noise control in [37], the combination of adaptive filters for relative navigation in [38], finite impulse response (FIR)-infinite impulse response (IIR) adaptive hybrid combination in [39], the affine combination of two adaptive sparse filters for estimating large-scale multiple-input multiple-output (MIMO) channels in [40], the combinations of multiple kernel adaptive filters in [41], the low-complexity approximation to the Kalman filter using convex combinations of adaptive filters from different families in [42], and the proposition of a family of combined-step-size proportionate filters in [43].…”