  Misspecifications (i.e. errors on the parameters) of state space models lead
to incorrect inference of the hidden states. This paper studies weakly
nonlin-ear state space models with additive Gaussian noises and proposes a
method for detecting and correcting misspecifications. The latter induce a
biased estimator of the hidden state but also happen to induce correlation on
innovations and other residues. This property is used to find a well-defined
objective function for which an optimisation routine is applied to recover the
true parameters of the model. It is argued that this method can consistently
estimate the bias on the parameter. We demonstrate the algorithm on various
models of increasing complexity.
