In　this　paper,　we　address　the　problem　of　the　joint　detection　and　estimation　fusion　in　distributed　multiple　sensor　systems.　The　objective　is　to　decide　between　two　hypotheses　and　make　the　corresponding　estimates　if　the　decided　hypothesis　has　unknown　parameters　at　the　same　time.　The　fusion　system　comprises　N　independent　sensors,　and　each　one　sends　a　binary　decision　to　the　fusion　center　for　integration.　The　traditional　approaches　treat　the　detection　and　estimation　separately,　and　tend　to　place　the　primary　emphasis　on　the　detection　part　through　maximizing　detection　probability　and　then　treat　the　estimation　part　as　suboptimal.　The　proposed　approach　treats　these　two　parts　simultaneously　in　a　jointly　optimal　manner.　The　average　estimated　cost　concerning　about　the　random　decision　rule　is　minimized　subject　to　the　corresponding　constraints　with　respect　to　the　false　alarm　probability　and　the　probability　of　missed　detection.　The　resulting　scheme　shows　that　a　trade-off　between　detection　and　estimation　performance　can　be　achieved.　A　hypothesis　test　example　with　unknown　parameters　based　on　multi-sensor　quantized　measurements　is　given,　which　demonstrates　the　flexibility　and　the　superiority　of　the　proposed　approach　compared　with　the　classical　Neyman-Pearson　and　the　GLRT.