Finite Cumulant Towers for Small-System-Noise Expansions in Nonlinear Filtering

Abstract: We study asymptotic expansions for nonlinear filtering in the small-system-noise regime. Our main result is that the expansion coefficients of conditional cumulants satisfy a closed finite-dimensional system that can be solved recursively. At each expansion degree, the evolution of a coefficient introduces a coefficient of the next higher cumulant rank, while a high-rank vanishing property terminates this hierarchy, yielding a finite triangular tower. The first layer is the Kalman--Bucy filter, and subsequent layers satisfy linear equations once the lower layers are known. To derive these equations, we establish generalized Zakai and Kushner--Stratonovich equations for observation-dependent random variables and obtain explicit coefficient equations in arbitrary dimension. The conditional cumulant expansion also determines the expansion of the conditional characteristic function. We further obtain an explicit bound on the number of scalar variables in terms of the state dimension and expansion degree. When the observation function vanishes, the construction reduces to ODEs for unconditional cumulant coefficients. Comparisons with existing asymptotic-expansion algorithms illustrate the resulting reduction in the number of ODE variables.