Sequential Monte Carlo estimation of aerosol size distributions

The estimation of time-varying aerosol size distributions on the basis of differential mobility particle sizer measurements is a dynamical inverse problem with a non-linear/non-Gaussian state space model. A sequential Monte Carlo approach for determining approximations for the state estimates is proposed. The vapour pressure, which is difficult to measure accurately, is here taken as an unknown state variable instead of assuming an approximate average value. Two simulation studies are carried out and the results are also compared with those obtained by using the extended Kalman filter which employs sequential Gaussian approximations based on the same state space model. The results show that in some cases the extended Kalman filter is a feasible approach while there are also evolution models for which the linear/Gaussian approximation is not sufficient.