An Incremental RLS for Distributed Parameter Estimation of IIR Systems Present in Computing Nodes of a Wireless Sensor Network

Two popular learning algorithms namely incremental least mean square (ILMS) and diffusion least mean square (DLMS) have already been reported in literature for distributed parameter estimation using the data collected from sensor nodes. The ILMS has long convergence problem for IIR systems. The DLMS has more communication overhead which leads to excess computational complexity. To alleviate this problem in this paper a fast incremental recursive least mean square algorithm (IRLS) is proposed for IIR systems present in the computing nodes of a wireless sensor network. Simulation results demonstrate that proposed algorithm provide faster convergence and accurate performance in two examples.