Improved Tracking for Distributed Signal Fusion Optimization in a Fully-Connected Wireless Sensor Network

The distributed adaptive signal fusion (DASF) algorithm is a generic algorithm that can be used to solve various spatial signal and feature fusion optimization problems in a distributed setting such as a wireless sensor network. Examples include principal component analysis, adaptive beamforming, and source separation problems. While the DASF algorithm adaptively learns the relevant second order statistics from the collected sensor data, accuracy problems can arise if the spatial covariance structure of the signals is rapidly changing. In this paper, we propose a method to improve the tracking or convergence speed of the DASF algorithm in a fully-connected sensor network with a broadcast communication protocol. While the improved tracking increases communication cost, we demonstrate that this tradeoff is efficient in the sense that an L-fold increase in bandwidth results in an R times faster convergence with
$\mathrm{R} > > \mathrm{L}$