Using an information filter to speed computation of sparse parameter estimates

This paper discusses the development of a recursive estimator which systematically arrives at sparse parameter estimates. Prior work achieved this by utilizing a Gaussian sum filter. This paper shows the relationship between the implementation using a Gaussian sum filter, where the mean and covariance of each component is propagated, and the equivalent representation using an information filter. We see that the information filter representation requires only a single information filter to be updated for each new measurement instead of the exponential number of measurement updates that were required when using the Gaussian sum filter. We thus see that using the information filter provides computational benefits when recursively estimating sparse parameters, reducing running time as well as data storage.