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Non-stationary additive noise modelling in direction-of-arrival estimation

The authors report a numerical investigation of a non-stationary noise model, based on the generalised autoregressive conditional heteroscedasticity time series, to perform a direction-of-arrival (DOA) estimation. In many of the studies reported to date, DOA estimation algorithms have been proposed in the presence of stationary noise. The performance of the previous algorithms such as conventional maximum likelihood (CML) and iterative maximum likelihood (IML) is reduced in many practical applications such as sonar where the noise shows a non-stationary behaviour. In this study, a modified IML estimation algorithm is proposed to include a non-stationary noise model. Underwater DOA estimation is studied as an example of DOA estimation in a non-stationary noise environment. To validate the noise model, the experimental data, measured by a single hydrophone 27 m underwater, is applied. The results indicate that the proposed algorithm is able to provide a significant improvement compared with CML and IML in such environments.