Sentence examples for sequential estimation from inspiring English sources

To illustrate the operation of the sequential estimation algorithm its MMSE estimates of the LOS and multipath delays are depicted in Figures 6 and 7, which correspond to two typical scenarios a navigation receiver has to cope with in urban environments.

The solution of the sequential estimation problem is obtained by the algorithm sequentially estimating the states of a system as a set of observations becomes available.

This method involves a sequential estimation process, where the target acceleration is estimated by coherently fusing all the signal observations mapped into the ambiguity function of the respective Doppler signature and detecting the combined peak via a direct search.

Second, when estimating the second threshold we adopt a sequential estimation procedure following Bai (1999).

For the former case we exhibit optimal static designs; our methods are then modified to handle the latter case, for which we give a sequential estimation method which is fully adaptive, yielding both consistent variance estimates and an asymptotically V-optimal design.

For the sequential Bayesian approach, the problem of multipath mitigation becomes one of sequential estimation of a hidden Markov process: the unknown channel parameters are estimated based on an evolving sequence of received noisy channel outputs.

By further comparisons, the sequential estimations by the adaptive RBPF with s k known are slightly better than the adaptive RBPF.

Section 3 presents the motion parameter estimation algorithms, including a brief review of the time-frequency analysis-based method and the description of the proposed techniques that are based on maximum likelihood (ML) and sequential estimations, both exploiting the group sparsity of target motion parameters.

The sequential estimation-adjusted urn (SEU) method is comparable with GDL in controlling the type I error.

When focusing on T MC and T ML, the generalized drop-the-loser urn (GDL) and sequential estimation-adjusted urn (SEU) have the best ability to attain the correct size of hypothesis test respectively.

The RAR procedures investigated in the present study are randomized play-the-winner (RPW) [ 8, 10], drop-the-loser (DL) [ 28], sequential maximum likelihood estimation (SMLE) [ 12], doubly-adaptive biased coin [ 2, 3], sequential estimation-adjusted urn (SEU) [ 13], and generalized drop-the-loser (GDL) [ 11] designs.