State estimation with destination constraints
Gongjian Zhou, Keyi Li · International Conference on Information Fusion · 2016
The problem of state estimation with a new constraint, named destination constraint here, in practical tracking scenario is considered. The anti-radiation missile (ARM) always moves towards the attacked target in an almost straight trajectory in X-Y plane due to the angle tracking guidance of the passive radar seeker (PRS). This implies a linear equality constraint to the motion of ARM, where the destination is known but the starting point unknown. To incorporate the destination constraint into tracking of ARM with measurements reported by radar, the position measurements of the ARM are also considered as measurements of the starting point and used to construct a noisy pseudo measurement of the implicit linear quality constraint. For properly filtering, the unscented transform (UT) is adopted to obtain the statistic properties of the pseudo measurement. Then the unscented Kalman filter (UKF) is involved to deal with the nonlinearity between states and augmented measurements. The proposed destination constraint Kalman filter (DCKF) is evaluated with a comprehensive comparison against two linear equality constraint estimation methods as well as two popular unconstrained nonlinear filtering methods. Monte-Carlo simulation results are presented to illustrate the effectiveness of DCKF in state estimation with destination constraint.