Abstract-In this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the statespace model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay. Index Terms-Extended Kalman filter (EKF), lateral flow immunoassay (LFIA), parameter estimation, particle filter, state estimation.
In REF , a new particle filter was developed to simultaneously identify both the states and parameters of the model.
Identification of Nonlinear Lateral Flow Immunoassay State-Space Models via Particle Filter Approach
IEEE Transactions on Nanotechnology
IEEE Transactions on Nanotechnology
Physics