Reconstruction　methods　for　discrete　data,　such　as　the　Moving　Least　Squares　(MLS)　and　Moving　Total　Least　Squares　(MTLS),　have　made　a　great　many　achievements　with　the　progress　of　modern　industrial　technology.　Although　the　MLS　and　MTLS　have　good　approximation　accuracy,　neither　of　these　two　approaches　are　robust　model　reconstruction　methods　and　the　outliers　in　the　data　cannot　be　processed　effectively　as　the　construction　principle　results　in　distorted　local　approximation.　This　paper　proposes　an　improved　method　that　is　called　the　Moving　Total　Least　Trimmed　Squares　(MTLTS)　to　achieve　more　accurate　and　robust　estimations.　By　applying　the　Total　Least　Trimmed　Squares　(TLTS)　method　to　the　orthogonal　construction　way　in　the　proposed　MTLTS,　the　outliers　as　well　as　the　random　errors　of　all　variables　that　exist　in　the　measurement　data　can　be　effectively　suppressed.　The　results　of　the　numerical　simulation　and　measurement　experiment　show　that　the　proposed　algorithm　is　superior　to　the　MTLS　and　MLS　method　from　the　perspective　of　robustness　and　accuracy.