Target　tracking　is　extremely　important　in　the　military　field.　In　multi-sensor　data　fusion　processing,　track　fusion　techniques　are　required　to　make　the　target　position　information　more　accurate　and　reliable.　In　this　paper,　the　target　motion　uses　uniform　and　uniformly　accelerated　motion　models,　and　the　smoothing　of　the　motion　trajectory　uses　Kalman　filtering.　In　this　paper,　Convex　combination　fusion　algorithm,　covariance　weighted　fusion　algorithm　and　adaptive　fusion　algorithm　are　used　to　fuse　the　track　data,　and　analyze　the　performance　of　these　three　algorithms　with　fusion　trajectory,　root　mean　square　error　of　fusion　comparison　and　algorithm　time　complexity　comparison.　The　simulation　results　show　that　all　three　algorithms　can　extract　useful　information　from　the　track　data　and　improve　the　target　tracking　accuracy.　©　2022　IEEE.