Navigation　accuracy　and　robustness　are　key　performance　indexes　of　automated　guided　vehicles　(AGVs).　In　our　previous　study,　the　magnetic　guidance　approach　based　on　magnetic　dipole　model　and　non-linear　optimization　algorithm　was　proposed,　which　has　high　positioning　accuracy　and　could　estimate　the　yaw　angle　of　AGV　directly.　However,　the　localization　accuracy　of　the　magnetic　guidance　approach　will　deteriorate　if　the　magnetic　nails　(MNs)　buried　in　the　ground　have　installation　errors　or　the　magnetic　moments　between　the　MNs　are　inconsistent.　To　overcome　this　problem,　we　propose　an　improved　method　based　on　error　analysis　and　prior　knowledge　for　the　magnetic　guidance　approach.　Firstly,　the　factors　that　affect　the　localization　accuracy　are　analyzed,　and　the　parameters　(　B_{\mathrm　{T}}　,　p　,　c　),　whose　errors　will　deteriorate　the　localization　accuracy,　are　combined　with　MN　pose　(　a　,　b　,　m　,　n　)　as　optimization　variables.　Then,　the　prior　knowledge　regarding　(　B_{\mathbf　{T}}　,　p　,　c　)　is　employed　to　construct　the　constraint　conditions　for　the　magnetic　guidance　approach.　Finally,　the　global　convergence　probability　and　convergence　speed　of　the　improved　magnetic　guidance　approach　are　analyzed.　Experimental　results　demonstrate　the　adaptability　and　robustness　of　the　improved　magnetic　tracking　approach,　which　diminishes　the　impact　of　MN　installation　errors　and　magnetic　moment　deviation.　The　parking　accuracy　of　AGV　is　improved　to　1.42±0.85　mm　and　1.10±0.38°.　©　2000-2011　IEEE.