Aiming at the dynamic target tracking problem in Wireless Sensor Networks(WSN) with limited sensing range, a collaborative dynamic target tracking algorithm in which Kalman Consensus Filter(KCF) is combined with dynamic clustering self-organization is proposed. The proposed algorithm uses a dynamic clustering protocol which consists of a cluster leader selection phase and cluster reconfiguration phase to limit messages exchanges between nodes participating in the target state estimation process. Then, a distributedly weighted estimation-prediction algorithm, namely Calman consensus filtering can be adopted to estimate the target state and predict its next position. This helps waking-up the most appropriate nodes to track the target and well organizing the network communications, while other nodes remaining in sleep state. The simulation results show that the proposed algorithm can not only reduce the average energy consumption of the network, but also improve the error estimation quality in the tracking process significantly compared with a centralized algorithm and other two kinds of distributed dynamic target tracking algorithms in common use.