In practical nonlinear filtering, the assessment of achievable filtering
performance is important. In this paper, we focus on the problem of efficiently
approximate the posterior Cramer-Rao lower bound (CRLB) in a recursive manner.
By using Gaussian assumptions, two types of approximations for calculating the
CRLB are proposed: An exact model using the state estimate as well as a
Taylor-series-expanded model using both of the state estimate and its error
covariance, are derived. Moreover, the difference between the two approximated
CRLBs is also formulated analytically.