The paper describes two modified implementations of unscented Kalman filter (UKF) and unscented particle filter (UPF) to solve nonlinear filtering problem for discrete-time dynamic space model (DSSM). DSSM is supposed to be nonlinear with additive Gaussian noise. The considered algorithm modifications are based on combination of UD-factorization of covariance matrices with sequential Kalman filter. The solution of tracking problem is illustrated for two cases. In the first case the problem of estimate of movable target coordinates from observed noised bearing is considered (a problem of passive location). In the second case the problem of an active location is described when noisy values of a distance to the accompanied object besides a bearing are available to the observer. Moreover, in the second case the motion model is extended by means of introducing a new parameter (a maneuver) such as an angle of velocity direction. To examine robustness of the considered algorithms in active target tracking problem (the second case) an arbitrary maneuver that differs from the initially given one in the motion model is considered as an observation.