A Novel Linear, Unbiased Estimator to Fuse Delayed Measurements in Distributed Sensor Networks with Application to UAV Fleet

Abstract

This paper proposes a novel methodology to fuse delayed measurements in a distributed sensor network. The algorithm derives from the linear minimum mean square error estimator and yields a linear, unbiased estimator that fuses the delayed measurements. Its performance regarding the estimation accuracy, computational workload and memory storage needs is compared to the classical Kalman filter reiteration that achieves the minimum mean square error in linear and Gaussian systems. The comparison is carried out using a simulated distributed sensor network that consists of a UAV fleet in formation flight in which the GPS measurements and relative positions are exchanged among neighboring network nodes. The novel technique yields similar performance to the reiterated Kalman filtering, which is the optimal linear Gaussian solution, while demanding less storage capacity and computational throughput in the problems of interest.