An　on-line　updating　method　of　Markov　transition　probability　for　the　interacting　multiple　model　(IMM)　algorithm　is　proposed,　and　the　square-root　cubature　Kalman　filter　(SRCKF)　is　introduced　into　IMM,　so　a　novel　time-varying　Markov　transition　IMM-SRCKF　algorithm　is　obtained.　Using　real-time　recursive　estimation　method　based　on　the　system　mode　information　implicit　in　the　current　measurements,　the　proposed　algorithm　effectively　avoids　the　problem　of　prior　determination　of　the　Markov　transition　probability　matrix　in　traditional　IMM.　Furthermore,　SRCKF　propagates　the　square　root　of　the　covariance　in　filter　interaction　so　that　it　guarantees　the　symmetry　and　positive　semi-definiteness　of　the　covariance　matrix　and　greatly　improves　the　numerical　stability　and　numerical　accuracy.　Simulation　results　show　that　the　proposed　algorithm　has　better　tracking　performance　and　higher　efficiency　compared　with　the　conventional　IMM　and　IMM-CKF.　©,　2015,　Chinese　Institute　of　Electronics.　All　right　reserved.