This article deals with the resilient implementation of parametrized linear filters (or controllers), i.e. realizations that are robust with respect to their implementation with fixed-point arithmetic. The implementation of a linear filter/controller in an embedded device is a difficult task because the numerical version of such algorithms suff ers from a deterioration in performances and characteristics. This degradation has two separate origins, corresponding to the quantization of the embedded coefficients and the round-off occurring during the computations. The optimal realization problem is to find, for a given filter, the most resilient realization. We here consider linear filters that depends on a set of parameters that are not exactly known during the design. They are used for example in automotive control, where a very late re-tuning is required. The paper presents results on FWL resiliency analyzis using interval optimization methods [2], and compare them to those obtained with the sensitivity approach.