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A new algorithm is proposed for estimating the state of a nonlinear stochastic system when only noisy observations of the state are available. The state estimation problem is formulated as a modal-trajectory, maximum likelihood estimation problem. The resulting minimization problem is analogous to the nonlinear tracking problem in optimal control theory. By viewing the system as an interconnection of lower-dimension subsystems and applying the so-called ε-coupling technqiue, which originated in the study of sensitivity of control systems to parameter variations, a near-optimal state estimation algorithm is derived which has the properties that all computations can be performed in parallel at the subsystem level and only linear equations need be solved. The principal attraction of the method is that significant reductions in the computational requirements relative to other approximate algorithms can be achieved when the system is large-dimensional.
Date of Publication: Jun 1975