By testing the bias vector as a target acceleration, the two-stage Kalman estimator can be used for tracking maneuvering targets. The first stage contains a constant velocity motion model and produces the target position and velocity estimates, while the second stage provides estimates of the target acceleration. When a maneuver is detected, the acceleration estimate of the second stage is used to correct the estimates of the first stage. In order to overcome the requirement for explicit maneuver detection in the two-stage estimator, the interacting acceleration compensation (IAC) algorithm is proposed. In the IAC algorithm, the two-stage estimator is viewed as having two acceleration models. The first model corresponds to the zero acceleration of the constant velocity model, while the second model is a constant acceleration model. Simulation results indicate that the tracking performance of the IAC algorithm approaches that of an interacting multiple model (IMM) algorithm with a constant velocity model and constant acceleration model, while requiring approximately 50% of the computations of the IMM algorithm.
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