Parametric methods for spatial signal processing in the presence of unknown colored noise fields

Two methods for estimation of noise correlations along an array of sensors are presented. Both rely on a parametric (autoregressive moving average) noise model. The model has the advantage of describing the noise correlations by a small number of parameters and can be applied to a great variety of physical noises. The first method is related to the calculation of the likelihood of whitened observations, and the second is related to Pisarenko's method (1973) applied to whitened observations. Both methods are obtained by optimization of a criterion and are iterative. The noise estimates can be used for sensor-output whitening and it then provides a means to improve array processing performance. The two methods perform well, both on simulated and real data. However, the first method seems simpler and more robust than the second.<
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