With　the　popularity　of　smart　devices　and　growing　demand　of　location-based　services,　machine　learning　methods　have　been　attracting　increasing　attention　for　their　potential　in　indoor　localization.　Since　GPS　signal　has　limited　access　in　indoor　environments,　alternative　sensing　solutions　have　been　employed,　among　which　the　integration　of　inertial　measurement　and　WiFi　Received　Signal　Strength　(RSS)　is　the　preferred　choice　for　its　low　cost　and　pedestrian　compatibility.　Researchers　have　proposed　various　approaches　incorporating　machine　learning　algorithms　to　improve　indoor　localization　performance,　which　can　be　broadly　divided　into　the　fingerprinting-based　approaches　and　ranging-based　approaches.　However,　these　conventional　methods　still　either　cannot　achieve　satisfactory　accuracy　or　need　the　assistance　of　other　prerequisites　to　reduce　the　localization　error.　To　address　this　issue,　in　this　chapter,　we　propose　a　new　indoor　localization　system　that　integrates　the　inertial　sensing　and　RSS　fingerprinting　via　a　modified　Particle　Swarm　Optimization　(PSO)-based　algorithm.　Different　from　traditional　methods,　our　proposed　method　improves　the　accuracy　by　a　new　optimization　process,　in　which　the　Inertial　Measurement　Unit　(IMU)　data　is　translated　into　the　displacement　information　serving　as　the　soft　constraints　of　the　optimization,　and　the　result　of　the　RSS　fingerprinting　method　provides　a　guide　for　the　swarm　search.　Also,　a　fitness　metric　based　on　Gaussian　Process　Regression　(GPR)　is　developed　to　evaluate　the　likelihood　of　each　particle　in　finding　the　real　position.　Experiments　are　conducted　in　the　real-world　scenarios,　and　the　results　validate　that　the　proposed　approach　outperforms　the　typical　approaches　by　at　least　15.8%　with　the　mean　localization　error　of　1.618　m.　©　The　Editor(s)　(if　applicable)　and　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2023.