This　paper　proposes　a　novel　multicycle　spectrum　sensing　method.　The　method　first　divides　the　received　data　samples　into　several　segments,　and　then　gets　cyclic　autocorrelation　estimates　from　these　segments,　constructs　a　test　statistic　from　these　estimates,　and　finally　makes　an　F-test　to　determine　whether　there　is　a　primary　signal　or　not.　The　method　can　use　multiple　cyclic　frequencies　simultaneously;　hence,　it　is　a　multicycle　detector.　The　method　is　also　nonparametric,　and　thus　it　is　robust　to　noise　uncertainty.　Compared　with　existing　cyclic　spectrum　sensing　methods,　the　proposed　method　can　reduce　computational　complexity　significantly　at　the　cost　of　a　little　performance　loss.　Simulation　results　are　given　to　show　the　validity　and　the　superiority　of　the　proposed　method.　©　2013　Springer　Science+Business　Media　New　York.