A Distributed Algorithm for Training Augmented Complex Adaptive IIR Filters

TL;DR

This paper introduces a distributed incremental algorithm for training augmented complex IIR filters in sensor networks, enabling efficient decentralized learning of complex-valued signals with improved performance over non-cooperative methods.

Contribution

The paper proposes the incremental augmented complex IIR (IACA-IIR) algorithm for decentralized adaptive learning of complex signals, incorporating augmented complex statistics for broader signal modeling.

Findings

The IACA-IIR algorithm outperforms non-cooperative ACAIIR in simulations.

The algorithm effectively models both proper and improper complex signals.

Performance validated on synthetic and real-world data.

Abstract

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