Sentence examples for matrix estimation strategies from inspiring English sources

In Section 3.2, we combined the multichannel AR parameter estimation algorithm with three covariance matrix estimation strategies: SCM, NSCM, and AML estimators, and then gave three adaptive CG-PGLRT detectors, where the true multichannel AR parameters Q and A H are substituted with the estimated ones.

Three covariance matrix estimation strategies using the secondary data are introduced to make derived receiver fully adaptive.

We apply three covariance matrix estimation strategies, i.e., sample covariance matrix (SCM), normalized sample covariance matrix (NSCM) and approximate ML (AML) estimator, to the multichannel AR parameter estimation procedure for estimating the unknown AR coefficient matrices.

For the two-step GLRT design criterion, we combine the multichannel AR parameter estimation algorithm with three covariance matrix estimation strategies for compound-Gaussian environment, then obtain three adaptive CG-PGLRT detectors by replacing the ideal multichannel AR parameters with their estimates.

For the mixing matrix estimation, the physics of the signal suggest introducing a novel strategy.

As shown in Figure 5, the key components of the whole processing strategy are band selection and sample covariance matrix estimation.

However, incremental estimation strategies usually require data smoothing and are known to produce biased parameter estimates.

Two estimation strategies are used.

Small sample covariance matrix estimation.

Traffic matrix estimation consists in inferring a traffic matrix from link-level measurements.

The singular problem of small sample inverse covariance matrix estimation.