Finally, the attitude estimation example is used to demonstrate the effectiveness of the proposed robust extended Kalman filter.
Finally, the proposed approach is illustrated by an attitude estimation example in an experimental case of noisy measurements which shows the efficiency of the proposed algorithm.
For instance, [19] presents a sparse estimation example of 1-D NMR (modal) data by using Lasso [20], LARS [21] and OMP [22].
MLFD as a distribution in a queuing system is given in Section 3. Reliability and stochastic ordering properties are discussed in Section 4. Section 5 deals with parameter estimation and examples of applications of MLFD.
In fact, (widehat {GS}) is inconsistent in the case of K=∞ or K=K n converging to ∞ too fast, and furthermore one cannot use the modern penalized estimation, for example lasso, to estimate p k, since there is no sparsity structure here.
It is well known that single-gene estimation (for example, ER) of individual pathway activity is not accurate enough to predict treatment outcomes (for example, response to ET).
These gains are more pronounced if less pilots are available for channel estimation, for example, when a fixed number of pilots is split between parallel data streams.
See Schlotter et al. (2011) for a non-technical discussion of IV estimation and example applications in the field of education.
Take the velocity estimation for example, from (13), we can see that the target velocity estimation accuracy depends on the choice of velocity step Δv= v i - vi-1.
Another advantage of this type of methods is the use of readily available information from the bitstream that is significant for the quality estimation, for example, the motion vectors, coding modes, and quantization parameter values.
