Abstract:
A new general two-stage algorithm is originally proposed
to reduce the computational effort for maneuvering target tracking in
mixed coordinates. The augmented state Kalman estimators, which are based
on the jerk modeling, are computationally expensive. The conventional
input estimation techniques assume constant acceleration level and there
are not covered a generalized input modeling. In this research, an innovative
scheme is developed to overcome these drawbacks by using a reduced state
Kalman estimator with a new structure, which is optimal for general conditions.
In addition, the proposed scheme is an unbiased filtering algorithm applied
in mixed coordinates based on the pseudo linear measurements.
A. Karsaz, H. Khaloozadeh, N. Pariz and A. Peiravi, 2008. Optimal Partitioned State Kalman Estimator for Maneuvering Target Tracking in Mixed Coordinates. Journal of Applied Sciences, 8: 3638-3645.
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