Recently, we proposed a finite-horizon robust Kalman filter (RKF) through designing and simultaneously minimizing the upper bounds of unknown covariances of prediction errors, filtering residuals and estimation errors.
Consensus in the presence of mismatches, read- and base-quality parameters were used as criteria to distinguish genetic variants from sequencing errors, filtering high-quality calls.
and where is given by the prediction error filter (5).
The position-error filter estimates errors in position, speed, and accelerometer bias.
We could also find a full solution for the orientation-error filter's process noise.
We employ fuzzy filters to implement the prediction-error filter, and the efficacy of the proposed method is illustrated by some examples.
In [17], echo cancellation is done by an adaptive LMS filter while a linear prediction error filter removes the residual echo and noise.
An alternative idea that avoids applying the backward filter was proposed by applying the error filter F on the regression vector.
BB-ITF was implemented using the fast least-mean square (FLMS) algorithm [55] based on a 256-tap prediction error filter.
To reduce this bias, [13] proposed a method of calculating those variables using a time-domain adaptive prediction error filter (PEF).
