Abstract
This paper deals with the problem of tracking a moving target by means of a network of geographically dispersed nodes with Doppler sensing, communication, and processing capabilities. The problem is addressed both in a hierarchical setting, i.e., in the presence of a fusion center that can fuse data from all the local nodes, and in a peer-to-peer setting wherein each node (peer) has to locally estimate the target kinematic state only on the grounds of local measurements and of data received from its neighbors. A suitable nonlinear observability decomposition is introduced in order to single out state coordinates that can be fully observed from a single Doppler sensor. Based on such a decomposition, a novel dual-stage filtering approach to distributed Doppler-only tracking is developed. In particular, it is shown how the proposed approach can be adopted both in the presence of a fusion center by means of covariance intersection and in the peer-to-peer case by exploiting consensus algorithms. The effectiveness of the proposed algorithms is evaluated via computer simulations.
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