  In this article, we present the elitist particle filter based on evolutionary
strategies (EPFES) as an efficient approach for nonlinear system
identification. The EPFES is derived from the frequently-employed state-space
model, where the relevant information of the nonlinear system is captured by an
unknown state vector. Similar to classical particle filtering, the EPFES
consists of a set of particles and respective weights which represent different
realizations of the latent state vector and their likelihood of being the
solution of the optimization problem. As main innovation, the EPFES includes an
evolutionary elitist-particle selection which combines long-term information
with instantaneous sampling from an approximated continuous posterior
distribution. In this article, we propose two advancements of the
previously-published elitist-particle selection process. Further, the EPFES is
shown to be a generalization of the widely-used Gaussian particle filter and
thus evaluated with respect to the latter for two completely different
scenarios: First, we consider the so-called univariate nonstationary growth
model with time-variant latent state variable, where the evolutionary selection
of elitist particles is evaluated for non-recursively calculated particle
weights. Second, the problem of nonlinear acoustic echo cancellation is
addressed in a simulated scenario with speech as input signal: By using
long-term fitness measures, we highlight the efficacy of the well-generalizing
EPFES in estimating the nonlinear system even for large search spaces. Finally,
we illustrate similarities between the EPFES and evolutionary algorithms to
outline future improvements by fusing the achievements of both fields of
research.
