A limitation common to all sequential Monte Carlo algorithms
is the computational demand of accurately describing an arbitrary
distribution, which may preclude real-time implementation for some
systems. We propose using interpolation to construct a high accuracy
approximation to the importance density. The surrogate density can
then be efficiently evaluated in place of sampling the true importance
density, allowing for the propagation of a large number of particles at
reduced cost. Numerical examples are given demonstrating the utility
of the approach.
