Energy consumption and tracking accuracy are two significant issues for collaborative tracking in Distributed Wireless Sensor Networks (DWSNs). To obtain a benefit from those issues, most of the recent work tends to reduce the spatial redundancy, while ignoring utilizing the attribute of time redundancy. In this paper, a novel energy-efficient framework of collaborative signal and information fusion is proposed for acoustic target tracking. The proposed fusion algorithm is based on neural network aggregation model and Gaussian Particle Filtering (GPF) estimation. And the Neural Network Based Aggregation (NNBA) can reduce spatial and time redundancy.