In　this　paper,　a　hybrid　extended　Kalman　filter　(EKF)　and　switching　particle　swarm　optimization　(SPSO)　algorithm　is　proposed　for　jointly　estimating　both　the　parameters　and　states　of　the　lateral　flow　immunoassay　model　through　available　short　time-series　measurement.　Our　proposed　method　generalizes　the　well-known　EKF　algorithm　by　imposing　physical　constraints　on　the　system　states.　Note　that　the　state　constraints　are　encountered　very　often　in　practice　that　give　rise　to　considerable　difficulties　in　system　analysis　and　design.　The　main　purpose　of　this　paper　is　to　handle　the　dynamic　modeling　problem　with　state　constraints　by　combining　the　extended　Kalman　filtering　and　constrained　optimization　algorithms　via　the　maximization　probability　method.　More　specifically,　a　recently　developed　SPSO　algorithm　is　used　to　cope　with　the　constrained　optimization　problem　by　converting　it　into　an　unconstrained　optimization　one　through　adding　a　penalty　term　to　the　objective　function.　The　proposed　algorithm　is　then　employed　to　simultaneously　identify　the　parameters　and　states　of　a　lateral　flow　immunoassay　model.　It　is　shown　that　the　proposed　algorithm　gives　much　improved　performance　over　the　traditional　EKF　method.