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An unconstrained IMM estimator consists of three main steps: (1) mixing/interacting of the mode-conditioned estimates in previous stage, (2) mode-conditioned state estimation, and (3) mode probability computation.
At last, node activeness estimator consists of AND-OR logical circuits as shown in Fig. 5.
The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown.
The designed estimator consists of two parts: an H∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters.
The multitaper spectrum estimator consists of windowed periodograms which are weighted together, where the weights are optimized using the Taylor expansion of the log-spectrum variance and a novel approximation for the log-spectrum bias.
Note that analogously to Section 5.1, we use newest vertex bisection and obtain h T ^ ≤ 2 − 1 / d h T for all T ∈ T and its successors T ^ ∈ T ^ ∖ T with T ^ ⊂ T. The explicit residual-based error estimator consists of the local contributions (5.11) η T (T ; V ) 2 ≔ h T 2 ‖ f ‖ L 2 (T ) 2 + h T ‖ [ ∂ t V ] ‖ L 2 (∂ T ) 2 for all T ∈ T.
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Its proper estimate is crucial for good performance of overall estimator, consisting of three coupled adaptive schemes.
To resolve these problems in the EKF, a multiple model adaptive estimator consisting of a bank of EKF in modal domain was designed, each filter in the bank is based on different Pex 0).
Local estimators consist of motion, color, and face detectors.
The GMB position estimator mainly consists of four modules such as sonar-based, sensor selection, sensor fusion, and sensor selection improved by combining it with sensor fusion.
Originally, to deal with dynamic panel fixed effects, Arellano and Bond (1991) proposed a "difference GM M estimator that consists in taking the first difference of all variables and regressing differences on differences to remove the country fixed effects.
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