General Recursive Minimum‐Variance Growing‐Memory Filter (Bayes and Kalman Filters Without Target Process Noise)
Eli Brookner DSc · 1998
In this chapter the author develops a recursive least-squares growing-memory filter that is not restricted to having the target trajectory approximated by a polynomial. The only requirement is that Yn−1, the measurement vector all time n−1, be linearly related to X1−1 in the error-free situation. The Bayes filter is derived and in turn from it the Kalman filter is again derive.