In the context of wireless sensor networks, a node's location must be known for its data to be meaningful in many cases. Received signal strength (RSS)-based localization has been widely used because of low complexity and easy deployment. This paper proposes a novel method to localize nodes in the presence of randomly moving obstructions. We introduce background learning to reduce interferences caused by moving obstructions such as people or other objects. Based on our experimental results, each link of data is modeled as a mixture of Gaussians (MoG) and its parameters are updated by background learning. In this way, we can reduce the interferences of moving obstructions from obtained RSS measurements. Then we use least-square (LS) cooperative localization algorithm to implement node localization and the experimental results show good performance.