Optimal Estimation With Missing Observations via Balanced Time-Symmetric Stochastic Models

Abstract

We consider data fusion for the purpose of smoothing and interpolation based on observation records with missing data. Stochastic processes are generated by linear stochastic models. The paper begins by drawing a connection between time reversal in stochastic systems and all-pass extensions. A particular normalization (choice of basis) between the two timedirections allows the two to share the same orthonormalized state process and simplifies the mathematics of data fusion. In this framework we derive symmetric and balanced Mayne-Fraser-like formulas that apply simultaneously to smoothing and interpolation.