Impulse control of observations in nonlinear Kalman filtering

This paper develops the impulse control approach to the observation process in nonlinear Kalman filtering problems, that results in introducing discrete observations into the continuous observation process. Impulsive modeling of the transition matrix in an observation equation generates online computable jumps of the estimate variance from its current position towards zero and, as a result, leads to instantaneous reduction of the estimate variance. Impulse observation control is useful in the filtering problems, where it is necessary to urgently eliminate undesirable behavior of the estimate variance or to ensure fast high-accuracy estimation on a short time interval (if the noise in observations is too high)