  In this paper we are concerned with the error-covariance lower-bounding
problem in Kalman filtering: a sensor releases a set of measurements to the
data fusion/estimation center, which has a perfect knowledge of the dynamic
model, to allow it to estimate the states, while preventing it to estimate the
states beyond a given accuracy. We propose a measurement noise manipulation
scheme to ensure lower-bound on the estimation accuracy of states. Our proposed
method ensures lower-bound on the steady state estimation error of Kalman
filter, using mathematical tools from eigen value analysis.
