Track　irregularity　is　the　main　excitation　source　of　the　vibration　of　railway　vehicle　system,　and　the　change　of　the　running　posture　of　the　car　body　frame　will　affect　the　detection　accuracy　of　track　geometric　irregularity.　In　theory,　the　running　posture　of　the　car　body　frame　can　be　measured　by　the　displacement　sensor,　but　in　most　cases,　the　measurement　is　difficult　and　the　accuracy　is　low.　When　the　acceleration　sensor　is　used　to　test,　there　are　various　kinds　of　noise　interference,　so　that　the　integral　displacement　contains　the　trend　term　drift　error.　To　solve　this　problem,　a　low-frequency　attenuation　frequency-domain　integration　method　was　proposed　in　this　paper　to　obtain　displacement　time　series　from　acceleration　data.　The　method　started　from　the　integration　of　acceleration　measurement,　and　used　the　least　square　method　based　on　moving　average　to　remove　the　error　caused　by　random　noise　in　the　integration　process　and　restored　the　actual　vibration　response　caused　by　track　irregularity.　Combined　with　the　moving　average　method,　high　pass　filter　and　frequency　domain　integration,　the　reconstructed　displacement　signal　was　obtained　through　multiple　rounds　of　smoothing,　filtering,　and　integration　to　avoid　signal　distortion.　The　main　advantage　of　this　method　was　that　there　was　no　long　period　drift　in　the　reconstructed　displacement.　The　accuracy　and　reliability　of　this　method　were　verified　by　comparing　with　the　measured　data　of　shaking　table.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.