This paper considers the problem of tracking real-world objects in a large scale area using distributed wireless sensor networks. Due to the limited power supply of wireless sensors, prediction based tracking mechanisms have been commonly used to conserve the energy consumption of the tracking algorithm. On the other hand, in order to preserve the quality of tracking (QoS), appropriate recovery approaches have to be incorporated into the tracking algorithm since the prediction may fail due to network topology changes, blind areas, the uncertainty and unpredictability of real-world objects' motion, etc. In this paper, a multi-modality tracking framework is proposed and an n-step prediction tracking algorithm is evaluated in the framework. The proposed framework is suitable for the tracking system in which sensors are randomly deployed. This paper exhibits how the network of multi-modality wireless sensors can reduce the power consumption of the tracking and preserve the quality of tracking as well.