In　this　article,　a　new　ZUPT　(zero　velocity　update)　algorithm　for　the　shoe-mounted　PNS　(personal　navigation　system)　based　on　low-precision　IMU　(inertial　measurement　unit)　has　been　studied.　Based　on　the　cascade　framework　of　Kalman　filtering　and　particle　filtering,　the　observability　analysis　on　the　state　variables　of　Kalman　filtering　in　the　bottom　layer　is　conducted,　and　the　error　of　course　angle　and　position　with　bad　observability　in　traditional　Kalman　filtering　is　thus　removed;　For　the　upper　particle　filter,　the　length　of　each　step　and　the　change　of　heading　angle　have　been　taken　as　the　variables　for　observation,　while　the　coordinates　of　heading　angle　and　horizontal　position　have　been　taken　as　the　state　variables,　to　build　the　dead-reckoning　motion　model　that　integrates　with　indoor　map　information.　The　ZUPT　algorithm　with　a　mutual　modification　between　Kalman　filtering　and　particle　filtering　has　been　designed.　Finally,　the　effectiveness　and　reliability　of　the　new　algorithm　have　been　verified　by　using　the　indoor　walking　data　of　low-precision　IMU.