Suboptimal design of discrete Kalman filter and smoother with redundant measurements

Abstract

In applications, there exist numerous stochastic dynamic systems whose measurements are redundantly available. The algorithm of discrete Kalman filter and smoother generally requires a heavy computational load. Taking advantage of the measurement redundancy, the suboptimal design of the discrete Kalman filter, and smoother with redundant measurements are presented here to reduce computational load in time and storage.