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In this paper we consider the problem of decentralized (distributed) adaptivelearning, where the aim of the network is to train the coefficients of a widelylinear autoregressive moving average (ARMA) model by measurements collected bythe nodes. Such a problem arises in many sensor network-based applications suchas target tracking, fast rerouting, data reduction and data aggregation. Weassume that each node of the network uses the augmented complex adaptiveinfinite impulse response (ACAIIR) filter as the learning rule, and nodesinteract with each other under an incremental mode of cooperation. Since theproposed algorithm (incremental augmented complex IIR (IACA-IIR) algorithm)relies on the augmented complex statistics, it can be used to model both typesof complex-valued signals (proper and improper signals). To evaluate theperformance of the proposed algorithm, we use both synthetic and real-worldcomplex signals in our simulations. The results exhibit superior performance ofthe proposed algorithm over the non-cooperative ACAIIR algorithm.

A Distributed Algorithm for Training Augmented Complex Adaptive IIR Filters