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Traditionally, in target tracking, much emphasis is put on the motion model that realistically represents the target's movements. We first present the classical constant velocity model and then introduce a new model that incorporates an acceleration component along the heading direction of the target. We also show that the target motion parameters can be considered part of a more general feature set for target tracking. This is exemplified by showing that target frequencies, which may be unrelated to the target motion, can also be used to improve the tracking performance. In order to include the frequency variable, a new array steering vector is presented for the direction-of-arrival (DOA) estimation problems. The independent partition particle filter (IPPF) is used to compare the performances of the two motion models by tracking multiple maneuvering targets using the acoustic sensor outputs directly. The treatment is quite general since IPPF allows general type of noise models as opposed to Gaussianity imposed by Kalman type of formulations. It is shown that by incorporating the acceleration into the state vector, the tracking performance can be improved in certain cases as expected. Then, we demonstrate a case in which the frequency variable improves the tracking and classification performance for targets with close DOA tracks.