Decentralized Delayed-State Information Filter (DDSIF): A new approach for cooperative decentralized tracking

摘要

This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. In particular, a decentralized delayed-state information filter (DDSIF) is described, in which full-state trajectories (that is, delayed states) are considered to fuse the information. This approach allows obtaining an estimation equal to that provided by a centralized system and reduces the impact of communication delays and latency in the estimation. The sparseness of the information matrix maintains the communication overhead at a reasonable level. The method is applied to cooperative tracking, and some results in disaster management scenarios are shown. In this kind of scenario, the target might move in both open-field and indoor areas, so the fusion of data provided by heterogeneous sensors is beneficial. The paper also shows experimental results with real data and integrating several sources of information.