# MLE or joint Kalman filter

I am trying to identify the parameters of a discrete-time nonlinear state space model: \begin{aligned} x_k & = f(x_{k-1},\theta)+q_{k-1}\\ y_k & = h(x_k,\theta)+r_k \end{aligned}

The MLE is one of the most commonly used estimators in statistics, another choice is to use joint Kalman filter where the unknown system states and parameters are concatenated in a single higher dimensional joint state vector.

Can someone give us an idea of what method should we use based on convergence and consistency?

• I have never heard of the term "joint Kalman filter." Do you have a reference? – Taylor Nov 14 '17 at 3:14