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The nonlinear filtering for a class of discrete-time stochastic dynamic systems whose measurement equations contain linear (or universal linearizable) components and nonlinear components which are mutually statistical independent is investigated. A two-step measurement update is proposed for the filtering of the systems. The first-step update is a linear (or universal linearization) measurement correction which introduces an intermediate estimate, while the second-step nonlinear linearization update produces the final posterior estimate based on the first-step estimate. Since the first measurement correction is a linear or universal linearization update, it provides an accurate linearization reference point for the second nonlinear measurement update. Two simulation examples show superiority of the new estimation method.
The nonlinear filtering for a class of discrete-time stochastic dynamic systems whose measurement equations contain linear (or universal linearizable) components and nonlinear components which are mutually statistical independent is investigated. A two-step measurement update is proposed for the filtering of the systems. The first-step update is a linear (or universal linearization) measurement correction which introduces an intermediate estimate, while the second-step nonlinear linearization update produces the final posterior estimate based on the first-step estimate. Since the first measurement correction is a linear or universal linearization update, it provides an accurate linearization reference point for the second nonlinear measurement update. Two simulation examples show superiority of the new estimation method.