Distributed parameter estimation of IIR system using diffusion particle swarm optimization algorithm

Abstract In wireless sensor networks (WSNs), distributed algorithms are used to estimate desired parameters for minimizing the communication overheads and make the network energy efficient. In literature, distributed estimation of finite impulse response (FIR) systems has been studied, because it is stable. In fact in many sensor network-based applications such as target tracking and fast rerouting, infinite impulse response (IIR) systems are required to be modeled. Thus, each sensor node uses the adaptive IIR filter and interact with each other under diffusion mode of cooperation to estimate the parameters. But the IIR filter inherently produces non-quadratic and multimodal error surfaces. Therefore gradient search algorithms that work well for FIR filters is not suitable for IIR system because they are likely to be trapped in the local minima. Keeping this in view, population based derivative free diffusion particle swarm optimization (DPSO) algorithms are proposed here to estimate the parameters of IIR systems. The algorithms are simulated for benchmark IIR systems and the steady state and transient performances are analyzed. The simulation results demonstrate that the proposed diffusion algorithms provide admirable improvement by resulting in faster convergence and low steady state value compared to that of conventional least mean squared algorithms.