Step counting-based dead-reckoning has been widely accepted as a cheap and\r\neffective solution for indoor pedestrian tracking using a hand-held device equipped with\r\nmotion sensors. To compensate for the accumulating error in a dead-reckoning tracking\r\nsystem, extra techniques are always fused together to form a hybrid system. In this paper,\r\nwe first propose a map matching (MM) enhanced particle filter (PF) as a robust\r\nlocalization solution, in which MM utilizes the corridor information to calibrate the step\r\ndirection estimation and PF is applied to filter out impossible locations. To overcome the\r\ndependency on manually input corridor information in the MM algorithm, as well as the\r\ncomputational complexity in combining two such algorithms, an improved PF is proposed.\r\nBy better modelling of the location error, the improved PF calibrates the location\r\nestimation, as well as step direction estimation when the map information is available,\r\nwhile keeping the computational complexity the same as the original PF. Experimental\r\nresults show that in a quite dense map constraint environment with corridors, the proposed\r\nmethods have similar accuracy, but outperform the original PF in terms of accuracy. When\r\nonly partial map constraints are applied to simulate a new testbed, the improved PF obtains\r\nthe most robust and accurate results. Therefore, the improved PF is the recommended DR\r\nsolution, which is adaptive to various indoor environments.
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