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Then, 2D DOA can be estimated by solving two 1D DOA estimation problems successively instead of directly estimating 2D DOA.
Luenberger type estimators are constructed for the H∞ and H2 estimation problems.
A posteriori error estimates and adaptive finite element approximation for parameter estimation problems have been obtained in [7, 8].
A new WL estimator has been given for solving general continuous-time estimation problems.
Moreover, an adaptive estimator is applied to combine the quantity data and target feature for estimation problems.
Ensemble Kalman (EnKF) filtering is an established framework for large scale state estimation problems.
Consensus-based distributed least square (LS) estimation problems have been studied in [112, 113].
For linear estimation problems, an exact covariance matrix can be determined in closed form.
Accordingly, many fruitful achievements on state estimation problems for neural networks have been reported [4 17].
Estimation problems in the presence of deterministic linear nuisance parameters arise in a variety of fields.
Compressed sensing-based techniques have been previously applied to estimation problems in networks[19 24].
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