DLF: Target Detection and Localization in Wireless Network

Abstract

The passive target detection and localization in wireless sensor networks(WSNs) is a critical issue in many applications. However, the data type been used for detection and localization is limited, due to the target does not carry any devices connected to any transmit(or receive). Thus, the great challenge is that how to use the limited data type for DLF working. In this paper, we analyze the wireless signal characteristic of the local stability, then design the "Double Level Fusion"(DLF) as the key structure of the designed system. DLF includes two layers confusion. The data level fusion is used for detecting the target while the decision level fusion is used for localizing the target. Combined with the receive signal strength indication (RSSI) characteristic of local stability, we propose the sliding average detection algorithm in data level fusion. We remedy the imperfection of data by using the advantage of Bayesian network in decision level fusion. Our approach examined by real experiment with square, grass, and woods, and the result demonstrates effective and accuracy for this work.