Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing

@article{Abeida2013IterativeSA,
  title={Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing},
  author={H. Abeida and Q. Zhang and J. Li and N. Merabtine},
  journal={IEEE Transactions on Signal Processing},
  year={2013},
  volume={61},
  pages={933-944}
}
  • H. Abeida, Q. Zhang, +1 author N. Merabtine
  • Published 2013
  • Mathematics, Computer Science, Engineering
  • IEEE Transactions on Signal Processing
  • This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose… CONTINUE READING
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