Adaptive infinite-impulse-response (IIR) filtering provides a powerful approach for solving a variety of practical signal processing problems. Because the error surface of IIR filters is typically multimodal, global optimisation techniques are generally required in order to avoid local minima. This contribution applies the particle swarm optimisation (PSO) to digital IIR filter design in a realistic time domain setting where the desired filter output is corrupted by noise. PSO as global optimisation techniques offers advantages of simplicity in implementation, ability to quickly converge to a reasonably good solution and robustness against local minima. Our simulation study involving system identification application confirms that the proposed approach is accurate and has a fast convergence rate and the results obtained demonstrate that the PSO offers a viable tool to design digital IIR filters. We also apply the quantum-behaved particle swarm optimisation (QPSO) algorithm to the same digital IIR filter design and our results do not show any performance advantage of the QPSO algorithm over the PSO, although the former does have fewer algorithmic parameters that require tuning.
['Sheng Chen', 'B.L. Luk']
Digital IIR filter design using particle swarm optimisation