SPICE: A Sparse Covariance-Based Estimation Method for Array Processing

By P. Stoica, Prabhu Babu, Jian Li. IEEE Transactions on Signal Processing, 2011

572 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data (2014, background): “According to [10], [11] and references therein, (24) is a suboptimal criterion, a new understanding of which will be provided in Subsection VI-C2.”
Sparse Methods for Direction-of-Arrival Estimation (2016, result): “Note also that, as compared to the original SPICE algorithm in [61, 62], a certain normalization step of the power estimates is removed here to avoid a global scaling ambiguity of the final power estimates (see also [58]).”
Sparsity-based DOA estimation using co-prime arrays (2013, methodology): “On the other hand, a parameter-free algorithm, referred to as the iterative adaptive approach (IAA), was proposed recently in [14] and extended in [15, 16, 17].”
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning (2017, methodology): “This method is referred to as cyclic minimization and appears in applications such as [37], [62], [93]–[96].”