Suboptimality of Cascaded and Federated Kalman Filters
Larry James Levy · 1996
Cascaded and Federated Filters are seldom optimal in the sense of the centralized Kahnan filter, yet no correct method of assessing their statistical performance has been used except for limited simulation testing. This paper develops the proper covariance(and mean) analysis algorithms for assessing the suboptimality of these implementations. “Dual state” suboptimal analysis is used to model the real world(truth) state vector along with the implemented “stacked” state vector of the first and second filters of the Cascaded filter approach(or the implemented “stacked” state vector of the parallel bank of local filters and following master filter of the Federated filter approach). Differences between the real world model and the stacked implemented filter models can then be statistically assessed. A number of specific examples relating to GPS/INS navigation are shown to illustrate the usefulness of the resulting statistical analysis algorithms. An novel approach for evaluating the dynamic lag of a GPS only(no INS) filter is also included.