Mobile Topology Learning of a Linear Dynamic Network

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In this paper, the problem of identifying the topology of a linear dynamic network using data acquired from a mobile agent is considered. Network topology learning is important in various areas where not only the direction of the node connections, but also the time-dependent characteristics of these connections need to be identified. Mobile-sensing related issues such as the measurement cost, however, have not yet been accounted for in the existing work. The proposed approach combines the Wiener filter technique with gradient-based optimization to minimize the total estimation error in the output (i.e., the node response), and then, the cost in terms of the total measurement time. A simulation example of identifying a 6-node network is presented to illustrate the proposed approach. The simulation results show that for the network of inter-node dynamics of a wide range of characteristics (time constants), both the direction, existence, and dynamics of the inter-node connections can be accurately identified by using the proposed approach.