Algorithms for Discrete Sequential Maximum Likelihood Bias Estimation and Associated Error Analysis

Optimization theory and discrete invariant imbedding is used in order to derive computationally efficient sequential algorithms for the maximum likelihood estimation of bias errors in linear discrete recursive filtering with noise corrupted input observations and correlated plant and measurement noise. Error analysis algorithms are derived for adaptive and nonadaptive systems with bias and modeling errors. Examples demonstrate the efficiency of the adaptive estimation algorithms and the error analysis algorithms for estimation with bias uncertainty.