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The standard state-dependent Riccati equation (SDRE) filter, which is set up by direct SDC parameterization, demands complete knowledge of the system model, and the disturbance inputs characteristics. However, this inherent dependency can severely degrade its performance in practical applications. In this paper, based on the HÂ¿ norm minimization criterion, a robust SDRE filter is proposed to effectively estimate the states of nonlinear uncertain systems exposed to unknown disturbance inputs. Considering a Lipschitz condition on the chosen SDC form, we guarantee fulfillment of a modified HÂ¿ performance index by the proposed filter. The effectiveness of the robust SDRE filter is demonstrated through numerical simulations where it brilliantly outperforms the usual SDRE filters in presence of model uncertainties as well as process and measurement noises.