Generalized sentinels defined via least squares

We address the problem of monitoring a linear functional (c, x)_Eof an unknown vectorx of a Hilbert spaceE, the available data being the observationz, in a Hilbert spaceF, of a vectorAx depending linearly onx through some known operatorA(E; F). WhenE=E ₁×E ₂,c=(c ₁ 0), andA is injective and defined through the solution of a partial differential equation, Lions (6]–8]) introduced sentinelssF such that (s, Ax)_Fis sensitive to x₁ E ₁ but insensitive to x₂ E₂. In this paper we prove the existence, in the general case, of (i) a generalized sentinel (s, ) ×E, where F withF dense in 80, such that for anya priori guess x₀ ofx, we have s, Ax + (, x₀)_E=(c, x)_E, where x is the least-squares estimate ofx closest tox ₀, and (ii) a family of regularized sentinels (s _n , _n ) F×E which converge to (s, ). Generalized sentinels unify the least-squares approach (by construction !) and the sentinel approach (whenA is injective), and provide a general framework for the construction of sentinels with special sensitivity in the sense of Lions 8]).