Abstract:The　inertial　navigation　system　based　on　micro-electro-mechanical-system　(MEMS)　has　received　extensive　concern　in　the　field　of　indoor　positioning.　In　order　to　solve　the　problem　of　positioning　precision　divergence　with　time,　scholars　proposed　the　solutions　of　fusing　indoor　map.　However,　in　current　indoor　map　matching　algorithms　there　exist　the　problems　of　low　correct　map-matching　rate,　large　computation　burden　and　etc.　In　order　to　improve　the　correct　map-matching　rate,　this　paper　presents　a　novel　map-matching　algorithm　based　on　conditional　random　field　model.　The　algorithm　adopts　closed　loop　design,　and　takes　the　optimal　matching　point　of　the　pedestrian　as　the　feedback　quantity　of　the　personal　navigation　system　to　correct　the　position　outputted　from　the　inertial　navigation　system.　The　position　correction　improves　the　accuracy　of　the　inertial　navigation　system　output　position　information;　then,　the　observation　point　coordinates　are　considered　as　a　feature　quantity　of　the　conditional　random　field　(CRF)　model.　The　extraction　of　the　coordinate　point　is　based　on　the　fixed　length　that　the　pedestrian　walks.　This　model　structure　reduces　the　number　of　the　state　point　extraction　compared　with　the　extraction　method　taking　step　length　as　the　coordinate　point　in　some　existing　literatures,　thereby　the　computation　burden　of　the　algorithm　is　reduced.　Multiple　experiment　results　show　that　the　proposed　algorithm　improves　the　correct　rate　of　map　matching.　©　2018,　Science　Press.　All　right　reserved.