Target Detection in Underwater Sensor Networks by Fusion of Active and Passive Measurements
Jing Yan, Zeqian Zhang, Xian Yang, Xiaoyuan Luo, Xinping Guan
Abstract
Underwater target detection plays an important role in the ocean exploration. However, the strong noise and weak communication properties of underwater environment make it challenging to achieve the detection task. This article is concerned with a non-cooperative target detection issue by fusion of active and passive measurements in underwater sensor networks (USNs). The detection procedure is mainly divided into two phases, i.e., local decision and external fusion. For the first phase, the active and passive measurements are both employed to develop a Chi-square test based local decision rule, where the membership functions are conducted to bias the weight of each measurement in accordance with signal reliability. Based on this, we formulate the communication topology of USNs as a two-layer multi-group (2LMG) structure, such that a hybrid Bayesian fusion algorithm is designed in the second phase to fuse the detection decisions from different sensor nodes. It is worth mentioning that, our solution can integrate the active and passive measurements by local decision, and more importantly, it can improve the detection accuracy and reduce the communication load through the 2LMG-based fusion algorithm. Finally, simulation results are performed to validate the effectiveness of our solution in this paper.