Input-Adaptive Models Based Multiple-Model Algorithm for Maneuvering Target Tracking

The dynamic models with multilevel inputs are adopted in a kind of multiple model estimatorfor highly maneuvering target tracking. While the target maneuvers with the continuous time-varying accelerations, the estimator increases the levels to improve thepercentage of coverage, which induces two problems: theincrease of calculation burden and the decrease of the estimation precision due to the competition between the models. A multilevel Input-adaptive multiple-model (IAMM)algorithm is proposed, in which the inputs are adjustedaccording to the prior value and the on-line estimated maneuver parameters by introducing a binary distribution.The adaptabilities of the inputs can depict the actual maneuver process better compared with the static multilevelinputs. The simulation proves the effectiveness of IAMMalgorithm compared with the IMM (Interacting multiplemodel) algorithm with models containing multilevel staticinputs.