The　quantitative　calculation　of　the　source　contribution　is　very　important　and　critical　for　the　identification　of　the　main　vibration　sources　and　the　reduction　of　vibration　and　noise　in　submarine.　It　is　difficult　to　calculate　the　source　contribution　because　of　the　submarine＇s　complex　structure　and　the　large　amount　of　vibration　sources.　As　a　typical　blind　source　separation　method,　independent　component　analysis　(ICA)　has　recently　been　proved　to　be　an　effective　method　to　solve　the　source　identification　problem　in　which　the　source　signals　and　mixing　models　are　unknown.　However,　the　outcomes　of　the　ICA　algorithm　are　affected　by　random　sampling　and　random　initialization　of　variables.　In　our　study,　the　prior　knowledge　of　the　vibration　sources　can　be　obtained　through　the　vibration　measurement　of　submarine.　Obviously,　information　in　addition　to　mixed　signals　from　sensors　can　lead　to　a　more　accurate　separation.　Therefore　the　contrast　function　of　ICA　can　be　enhanced　by　the　reference　signals　obtained　by　the　prior　knowledge.　In　this　paper,　a　closeness　measurement　between　the　independent　components　and　the　reference　signals　obtained　by　the　prior　knowledge　is　introduced,　and　the　closeness　measurement　is　constructed　to　have　the　same　optimization　direction　with　the　traditional　contrast　function:　negentropy.　The　closeness　measurement　is　used　to　enhance　the　contrast　function　and　then　the　enhanced　contrast　function　is　optimized　by　means　of　the　Newton　iteration　and　the　deflation　approach.　Thus　the　simplified　independent　component　analysis　with　reference　(ICA-R)　algorithm　is　obtained.　After　that　a　method　to　quantitatively　calculate　the　source　contribution　is　proposed　based　on　the　outcomes　of　the　simplified　ICA-R.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　by　the　numerical　simulation　studies.　The　performance　offered　by　the　proposed　method　is　also　investigated　by　the　experiment:　it　appear　as　a　very　appealing　tool　for　the　quantitative　calculation　of　the　source　contribution.