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We develop a rational function macromodeling algorithm named warped impulse structure estimation for macromodeling of system responses with time-sampled data. The ideas of digital filter design, Walsh theorem, and complementary signal are introduced to convert the macromodeling problem into a non-pole-based Steiglitz-McBride iteration without initial guess and eigenvalue computation. Furthermore, we introduce frequency warping as a preprocessing step to improve the numerical condition in the computation. We demonstrate the fast convergence and the versatile macromodeling requirement adoption through a P-norm identification expansion, using examples from practical structures.