Underwater target detection plays an important role in the ocean exploration. However, the strong noise and weak communication properties of the underwater environment make it challenging to achieve the detection task. This chapter is concerned with a non-cooperative target detection issue by fusion of active and passive measurements in maritime unmanned platforms (MUSs). 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 MUSs 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, the fusion 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 chapter.