# State space estimation with state dependent state variance

I am estimating a state space of the following form $$Y_t= A X_t + \epsilon_t$$ $$X_{t+1} = B X_{t} + \sigma \sqrt{( a-X_t)(X_t-b)} \eta_t$$

Considering the variance of the state error is state dependent and non-linear, Can we apply normal Kalman filter for the likelihood estimation? If not, what is the most convenient alternative?