… to derive the Kalman filter in presence of bias. The first … the bias estimate is simply an add-on
to the normal Kalman filter. It … difference between the bias and the random error is quantified …

… three-point model in (33) is that it produces biased estimates in the presence of noise [51]. …
trates frequency estimation in the presence of random spike noise, which models the …

… of a Kalman filter residual are computed for specific cases in which the Kalman filter model
differs … Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a …

… this is done, standard Kalman filter programs can be applied … It is thus known that the method
may give biased estimates, eg… ), and we have thus solve the bias problems as well as the …

… considering the presence of … be biased and in general sub-optimal, like the EKF. The reason
for this is the effect of the linearisation error that is a random variable that may have a bias (…

… sensor biases. The commonly used decoupled Kalman filtering method [26] for sensor bias
… is not modeled as an unknown constant but as a random sequence as in Section IIB, under …

… function that measures both the bias and the random error. The bias is estimated by the …
The main difference here is the presence of bias. It means that the ensemble value of the …

… The presence of the stochastic errors in MEMS (Micro Electro … to estimate and compensate
the random drift of MEMS … The deterministic errors including bias, misalignment, and scale …

… Kalman filter [9–11], improves covariance fidelity in the presence of sensor measurement
biases… This should not be surprising, because Kalman filters are designed to attenuate random …

… presence of attenuation biases is the norm rather than the exception in the real world. Therefore,
reducing such bias … , purely random errors tend to cancel out whereas systematic biases …