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Hence, we obtain a general form of the linear estimator, appropriate for Rician fading channels, that minimizes the estimation error of channel matrix.
The design of the classifier consists in finding the vector v that minimizes the estimation error.
To tackle the above problem, we first introduce a noise reduction operator, Ψ, which minimizes the estimation error of a cost function.
The algorithms considered include a Kalman filter that minimizes the estimation error variance, an H∞ filter that minimizes the worst-case estimation error, and a robust mixed Kalman/H∞ filter that allows for uncertainties in both the system and measurement matrices.
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The computation is based on a distributed optimization approach that guarantees the stability of the estimator while minimizing the estimation error variance.
Based on this model, MMSE estimators minimize the estimation error variance conditioned on the observations.
Based on this, assuming identical noise variances on all subcarriers and optimizing the weights to minimize the estimation variance under the constraint of an unbiased estimator, it can be shown that the MRC weights corresponding to the different subcarriers must be proportional to | Ĉ k |.
It has been shown in [48] that the estimate N ̂ opt of N opt can be found via (108) to minimize the estimation error of all of the states or the k th one as, respectively, N ̂ opt ≅ arg min n ∂ ∂n tr V n + 1, (109).
In addition, as the SDP method can achieve global minimal, if necessary, the estimated target position can be used to re-select the anchor Rx node to minimize the estimation error.
The optimal training sequence that can minimize the estimation MSE was also derived.
Instead of directly minimizing the estimation error, we choose to use an indirect metric related to the posterior distribution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com