Maneuvering target tracking is an important application in wireless sensor network (WSN). Usually, Kalman filter (KF) or extended Kalman filter (EKF) is used to predict and estimate target states. However, when a target has high maneuverability, KF or EKF always does not work well. In this paper, we employ distributed interactive multiple model (IMM) filter to estimate target position and velocity. A novel dynamic grouping idea is proposed and we apply it to dynamic-group scheduling scheme (DGSS), which is used to schedule next tasking node. Simulation results show that, compared with EKF, distributed IMM filter can achieve significant improvement on tracking accuracy for target tracking in WSN. At the same time, DGSS, which adopts changing sampling intervals and a dynamicgroup Scheduling idea, receives a superior performance in realtime property compared with the adaptive sensor scheduling strategy without energy consumption degraded.