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This paper presents a novel algorithm for efficient online estimation of thefilter derivatives in general hidden Markov models. The algorithm, which has alinear computational complexity and very limited memory requirements, isfurnished with a number of convergence results, including a central limittheorem with an asymptotic variance that can be shown to be uniformly boundedin time. Using the proposed filter derivative estimator we design a recursivemaximum likelihood algorithm updating the parameters according the gradient ofthe one-step predictor log-likelihood. The efficiency of this online parameterestimation scheme is illustrated in a simulation study.

Particle-based, online estimation of tangent filters with application to parameter estimation in nonlinear state-space models