The set-bounded joined parameter and state estimation algorithm generates robust estimates of these parameters and state for Model Predictive Control purposes.
The aim is to maximise the precision of estimates of these parameters (or, alternatively, to minimise bias in their estimation), so that residual uncertainty associated with estimates of incremental cost-effectiveness (joint costs and effects) is minimised.
Estimates of these parameters made from extrapolating laboratory core (MHz) or wireline (kHz) data to lower frequencies can be highly unreliable.
Due to the difficulties of acquiring reliable and accurate estimates of these parameters in the field, an original evaluation approach was chosen that reproduces the TLiDAR scanning and subsequent tree reconstruction process in a virtual environment.
We resort to a design procedure based upon the generalized likelihood ratio test (GLRT): first we derive the GLRT assuming that the textures are known, then we plug into the derived test suitable estimates of these parameters.
Note that, when applying Eq. (11), only population-based estimates of these parameters were used.
In a first step, pilot-based estimates of these parameters are obtained.
Once the proper estimates of these parameters have been obtained, they can be frozen and used for subsequent retrieval experiments.
Geophysical and geochemical techniques used on volcanoes may all lead to estimates of these parameters, sometimes with direct measurements, mostly with modeling and strong hypothesis.
In practice, if σ si 2 and k are estimated, the obtained estimates of these parameters are to be used in (5) and (6).
Especially at low iSNRs, online estimates of these parameters are strongly biased and hard to obtain [34], which is not reflected in the evaluation in Fig. 8.
