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Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model
Author(s) -
Stéphanie Bidon,
Marie Lasserre,
François Le Chevalier
Publication year - 2018
Publication title -
ieee transactions on aerospace and electronic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.137
H-Index - 144
eISSN - 1557-9603
pISSN - 0018-9251
DOI - 10.1109/taes.2018.2848360
Subject(s) - clutter , gibbs sampling , bayesian probability , autoregressive model , computer science , algorithm , grid , radar , noise (video) , artificial intelligence , bayesian inference , pattern recognition (psychology) , mathematics , statistics , telecommunications , geometry , image (mathematics)
The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch, and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.

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