Communication-Aware Distributed Estimation Over a Network of Aerial Vehicles

This article investigates the problem of distributed estimation over a network of multiple aerial vehicles with limited communication bandwidth. By introducing the concept of partial update, an information-weighted consensus filter with partial information exchange (ICF-PIE) is first designed to reduce the bandwidth of signals transmitted. In this algorithm, only a subset of the information vector/matrix of each aerial vehicle node is selected to transmit to the locally connected nodes, deducing the communication burden. On this basis, the fixed-step consensus-based ICF-PIE (FS-ICF-PIE) is developed to further solve the ideal hypothesis of infinite consensus iterations at each time step, which is generally required in consensus-based algorithms. Theoretical analysis reveals that the proposed distributed tracking algorithm can achieve convergence to the optimal centralized Kalman filter (CKF) while reducing the network communication bandwidth. Numerical simulations, as well as outdoor flight experiments, are relatively conducted to validate the effectiveness of the proposed FS-ICF-PIE algorithm and the related theoretical findings.