The　paper　presents　a　smart　integrated　navigation　algorithm　based　on　neural　network-aided　observation,　aiming　at　the　divergence　problem　of　integrated　navigation　systems　caused　by　global　positioning　system　(GPS)　outages.　The　method　trains　the　neural　network　when　the　GPS　is　available,　and　it　independently　rebuilds　the　integrated　navigation　system　using　the　neural　network　when　GPS　outages　occur.　The　output　of　the　neural　network　is　utilized　as　the　measurement　to　build　a　new　Kalman　filter,　which　is　used　to　amend　the　error　of　the　strapdown　inertial　navigation　system　(SINS),　and　then　the　continuous　navigation　with　high　precision　is　realized.　The　simulation　was　carried　out.　The　result　demonstrated　that　the　divergence　of　attitude,　velocity　and　position　were　effectively　controlled　under　this　algorithm.　The　precision　and　reliability　of　integrated　navigation　systems　were　improved.