Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Maximum Likelihood DOA Estimation of Multiple Wideband Sources in the Presence of Nonuniform Sensor Noise | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 835079 12 pages doi 2008 835079 Research Article Maximum Likelihood DOA Estimation of Multiple Wideband Sources in the Presence of Nonuniform Sensor Noise C. E. Chen F. Lorenzelli R. E. Hudson and K. Yao Los Angeles EE Department University of California Los Angeles CA 90095 USA Correspondence should be addressed to C. E. Chen chiaoen@ Received 1 March 2007 Revised 21 July 2007 Accepted 8 October 2007 Recommended by Sinan Gezici 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 Cramer-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 wideband 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 loglikelihood 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. Copyright 2008 C. E. Chen et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is .
