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Personal dead reckoning (PDR) localization technology can provide effective and critical assistance for public security, such as emergency rescue or anti-terror training in the indoor or underground environment without the need of deploying additional positioning infrastructure. However, the PDR suffers from the severe position error accumulation with time due to the inaccurate step length and moving direction estimation. To improve the self-positioning accuracy, this paper proposed a novel indoor self-localization algorithm using two kinds of automatic calibration methods, i.e., opportunistic magnetic trajectory matching and indoor landmark identification. Extensive experiments performed in two representative indoor environments, including an office building and a supermarket, demonstrate that the proposed self-localization algorithm can obtain an 80 percentile localization accuracy of 1.4m and 2m in the two representative indoor environments, respectively, which outperforms the art-of-the-state PDR algorithms.
Wang et al. (2016) studied this question.