Insomnia　is　the　most　common　sleep　disorder　linked　with　adverse　long-term　medical　and　psychiatric　outcomes.　Automatic　sleep　staging　plays　a　crucial　role　in　aiding　doctors　to　diagnose　insomnia　disorder.　Only　a　few　studies　have　been　conducted　to　develop　automatic　sleep　staging　methods　for　insomniacs,　and　most　of　them　have　utilized　transfer　learning　methods,　which　involve　pre-training　models　on　healthy　individuals　and　then　fine-tuning　them　on　insomniacs.　Unfortunately,　significant　differences　in　feature　distribution　between　the　two　subject　groups　impede　the　transfer　performance,　highlighting　the　need　to　effectively　integrate　the　features　of　healthy　subjects　and　insomniacs.　In　this　paper,　we　propose　a　dual-teacher　cross-domain　knowledge　transfer　method　based　on　the　feature-based　knowledge　distillation　to　improve　the　performance　of　sleep　staging　for　insomniacs.　Specifically,　the　insomnia　teacher　directly　learns　from　insomniacs　and　feeds　the　corresponding　domain-specific　features　into　the　student　network,　while　the　health　domain　teacher　guide　the　student　network　to　learn　domain-generic　features.　During　the　training　process,　we　adopt　the　OFD　(Overhaul　of　Feature　Distillation)　method　to　build　the　health　domain　teacher.　We　conducted　the　experiments　to　validate　the　proposed　method,　using　the　Sleep-EDF　database　as　the　source　domain　and　the　CAP-Database　as　the　target　domain.　The　results　demonstrate　that　our　method　surpasses　advanced　techniques,　achieving　an　average　sleep　staging　accuracy　of　80.56%　on　the　CAP-Database.　Furthermore,　our　method　exhibits　promising　performance　on　the　private　dataset.