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ML estimation and CRB for narrowband AR signals on a sensor array

This paper considers the exploitation of temporal correlation in incident sources in a narrowband array processing scenario. The MLE and CRB are derived for parameter estimation of spatially uncorrelated first order Gaussian autoregressive source signals with additive Gaussian spatially and temporally uncorrelated sensor noise. These are compared to the MLE and CRB for the usual uncorrelated (WN) sources model. The paper deals with the case where the number of data snapshots is small. Numerical simulations show that (i) there is no significant performance gain in the correlated signal case, and significantly, (ii) the WN MLE performance does degrade in the presence of source correlation, which appears to be in contrast to some recently published work.