Maximum likelihood DOA estimation of multiple wideband sources in the presence of nonuniform sensor noise

  • Authors:

  • C. E. Chen;F. Lorenzelli;R. E. Hudson;K. Yao

  • Affiliations:

  • Los Angeles EE Department, University of California, Los Angeles, CA;Los Angeles EE Department, University of California, Los Angeles, CA;Los Angeles EE Department, University of California, Los Angeles, CA;Los Angeles EE Department, University of California, Los Angeles, CA

  • Venue:
  • EURASIP Journal on Advances in Signal Processing
  • Year:
  • 2008

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Abstract

We investigate the maximum likelihood (ML) direction-of-arrival (DOA) estimation of multiple wideband sources in the presence of unknown nonuniform sensor noise. New closed-form expression for the direction estimation Cramér-Rao-Bound (CRB) has been derived. The performance of the conventional wideband uniform ML estimator under nonuniform noise has been studied. In order to mitigate the performance degradation caused by the nonuniformity of the noise, a new deterministic wide-band nonuniform ML DOA estimator is derived and two associated processing algorithms are proposed. The first algorithm is based on an iterative procedure which stepwise concentrates the log-likelihood function with respect to the DOAs and the noise nuisance parameters, while the second is a noniterative algorithm that maximizes the derived approximately concentrated log-likelihood function. The performance of the proposed algorithms is tested through extensive computer simulations. Simulation results show the stepwise-concentrated ML algorithm (SC-ML) requires only a few iterations to converge and both the SC-ML and the approximately-concentrated ML algorithm (AC-ML) attain a solution close to the derived CRB at high signal-to-noise ratio.