Constrained state estimation using noisy destination information

Tracking performance can be improved by the incorporation of a destination constraint. However, directly using available noise-corrupted destination information in the existing destination constrained filtering methods may lead to performance degradation. To address this limitation, in this paper, the true destination is also treated as a state to be estimated along with the target state. Then, the destination constraint information is leveraged by using the relationship between the state components and a newly constructed pseudo-measurement. An uncertain destination constrained augmented state filter (UDC-ASF) with its state being augmented by destination, in which the unscented Kalman filter (UKF) is used to deal with the strong nonlinearity of the measurements, is proposed to produce both constrained target state and destination estimates. Moreover, the unknown slope and the intercept of the straightline representing the destination constraint can also be augmented into the state vector and two pseudo-measurements are constructed in the process. The corresponding UDC-ASF with slope and intercept (UDC-ASF-SI) is derived. The a priori noisy destination is used to initialize the two proposed filters. An extension of the UDC-ASF to track multiple targets heading to the same destination is also discussed. Simulation results demonstrate the effectiveness of the proposed methods.