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The moving horizon estimation (MHE) technique combined with a Luenberger observer is used to design a state estimator with which the state estimation error converges to and remains in some disturbance invariant set.
A more robust quantile estimator with which to investigate these suggested lines of inquiry is proposed by Canay (2011).
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nBecause the maximum likelihood estimator (MLE) may have difficulty converging, especially with multiple endogenous variables, I estimate Newey's [31] minimum chi-squared estimator (two-step estimator), which is less efficient than the MLE.
Aiming to solve the problem, we address a novel timing estimator with three steps, which could decrease the impact of threshold in a great deal, promote the flexibility for both preambles 1 (or 2) and 3 (or 4), enhance the stability of estimators, as well as the uniqueness of timing sample.
The detailed derivation for IIB-phase estimation using S-transform is given in Appendix 4. This section compares the performance of both AW-XWVD and S-transform as a phase estimator with the CRLB which is often used as a benchmark [41], as it gives the theoretical lower limit to the variance of any unbiased parameter estimator [42].
We also consider two other consistent estimators, namely: the HC3 estimator, which is an approximation to the jackknife estimator, and the weighted bootstrap estimator.
Joint data and channel estimation can be realized by employing a Viterbi detector along with channel estimators, which estimate the channel impulse response estimation over frequency selective Rayleigh fading channels.
These extrinsic probabilities are updated by the decoder and exchanged in an iterative fashion with the estimator which, on its turn, updates the phase estimates.
(In this article, the term accurate estimate will be used synonymously with the statistical term consistent estimator, which is an estimator that converges to the correct value as the sample size goes to infinity).
The pose estimator, which estimates poses with the extracted silhouette in the recognition stage, must recognize multiple poses in real time in a mobile environment.
The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes.
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