Hybrid consensus‐based distributed pseudomeasurement information filter for small UAVs tracking in wireless sensor network

Abstract

In this study, a novel state estimation method is proposed for small unmanned aerial vehicles (UAVs) tracking in passive sensors (PSs) wireless sensor network (WSN), where the measurement equations are highly non-linear. Different from many non-linear function approximation methods, the non-linear measurement functions are recast into a linear form with respect to UAV states. The centralised 3D pseudomeasurement information filter (PMIF) is derived by embedding the linear form measurements into conventional information filtering schemes. For distributed state estimation in a not fully connected WSN, a hybrid consensus-based distributed PMF (DHCPMIF) is developed in which each PS independently calculates local contribution by using its own pseudomeasurement. To guarantee bounded estimation error as close as possible to that obtained by the optimal centralised estimator, the hybrid consensus method is developed by combining consensus on measurement and consensus on information method, which retains the advantages of both approaches, thus providing estimation accuracy improvement when the number of consensus iterations is moderate. The effectiveness of the proposed distributed filter is verified via two simulation examples involving tracking a UAV using four and eight PSs, respectively.