The Kalman filter has become a cornerstone in the design of tracking loops for Global Navigation Satellite System (GNSS) receivers, offering optimal state estimation by combining dynamic models with ...

It appears that no particular approximate [nonlinear] filter is consistently better than any other, though . . . any nonlinear filter is better than a strictly linear one. 1 The Kalman filter is a ...

This section describes a collection of Kalman filtering and smoothing subroutines for time series analysis; immediately following are three examples using Kalman filtering subroutines. The state space ...

If you program using values that represent anything in the real world, you have probably at least heard of the Kalman filter. The filter allows you to take multiple value estimates and process them ...

If you’re looking to improve the stability of your self balancing robot you might use a simple horrifying equation like this one. It’s part of the journey [Lauszus] took when developing a sensor ...

The time series analysis subroutines are an adaptation of parts of the TIMSAC (TIMe Series Analysis and Control) package developed by the Institute of Statistical Mathematics (ISM) in Japan.

Using Kalman equations, we derive straightforward formulas for the total imputation variance for several imputation methods commonly used in regression analysis and (un)equal probability sampling ...