For　separation　and　reconstruction　of　source　signals　from　observed　signals　problem,　the　physical　significance　of　blind　source　separation　modal　and　independent　component　analysis　is　not　very　clear,　and　its　solution　is　not　unique.　Aiming　at　these　disadvantages,　a　new　linear　and　instantaneous　mixing　model　and　a　novel　source　signals　separation　reconstruction　solving　method　from　observed　signals　based　on　principal　component　analysis　(PCA)　are　put　forward.　Assumption　of　this　new　model　is　statistically　unrelated　rather　than　independent　of　source　signals,　which　is　different　from　the　traditional　blind　source　separation　model.　A　one-to-one　relationship　between　linear　and　instantaneous　mixing　matrix　of　new　model　and　linear　compound　matrix　of　PCA,　and　a　one-to-one　relationship　between　unrelated　source　signals　and　principal　components　are　demonstrated　using　the　concept　of　linear　separation　matrix　and　unrelated　of　source　signals.　Based　on　this　theoretical　link,　source　signals　separation　and　reconstruction　problem　is　changed　into　PCA　of　observed　signals　then.　The　theoretical　derivation　and　numerical　simulation　results　show　that,　in　despite　of　Gauss　measurement　noise,　wave　form　and　amplitude　information　of　unrelated　source　signal　can　be　separated　and　reconstructed　by　PCA　when　linear　mixing　matrix　is　column　orthogonal　and　normalized;　only　wave　form　information　of　unrelated　source　signal　can　be　separated　and　reconstructed　by　PCA　when　linear　mixing　matrix　is　column　orthogonal　but　not　normalized,　unrelated　source　signal　cannot　be　separated　and　reconstructed　by　PCA　when　mixing　matrix　is　not　column　orthogonal　or　linear.　©　2014　IFSA　Publishing,　S.　L.