The stability properties of matrix-valued Riccati diffusions are investigated. The matrixvalued Riccati diffusion processes considered in this work are of interest in their own right, as a rather prototypical model of a matrix-valued quadratic stochastic process. In addition, this class of stochastic models arise in signal processing and data assimilation, and more particularly in ensemble Kalman-Bucy filtering theory. In this context, the Riccati diffusion represents the flow of the sample covariance matrices associated with McKean-Vlasov-type interacting Kalman-Bucy filters. Under rather natural observability and controllability conditions, we derive time-uniform moment and fluctuation estimates and exponential contraction inequalities. Our approach combines spectral theory with nonlinear semigroup methods and stochastic matrix calculus. The analysis developed here applies to filtering problems with unstable signals. This analysis seem to be the first of its kind for this class of matrix-valued stochastic differential equation.1/2This special case (κ = 0) defines, in some sense, a minimal prototype of a forward-in-time matrixvalued Riccati diffusion in the space of symmetric positive (semi-)definite matrices. We let φ ǫ t (Q) := Q t be the stochastic flow of the matrix diffusion equation (1.2). Whenever it exists, the inverse stochastic flow of (1.2) is denoted by φ −ǫ t (Q) := Q −1 t . For any 0 ≤ s ≤ t, we let E ǫ s,t (Q) be the transition semigroup associated with the flow of random matrices [A − φ ǫ t (Q)S], i.e. the solution of the forward and backward equationsfor any 0 ≤ s ≤ t, with E ǫ t,t (Q) = I. When s = 0 we write E ǫ t (Q) instead of E ǫ 0,t (Q). We write φ t (Q) and E s,t (Q) instead of φ 0 t (Q), and E 0 s,t (Q), to denote the flow of the deterministic matrix Riccati differential equation when ǫ = 0, and the exponential semigroup defined via φ t (Q).
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