Abstract
Realizing a reliable and robust localization based on mobile nodes plays a critical role in increasing pervasive sensing environments and location-based services (LBS). Although the Global Positioning System (GPS) has been widely used in outdoor environments, indoor robust positioning is still a challenging problem because of the unavailability of GPS and complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve the problem, an accurate and robust integration localization scheme based on Kalman filter is proposed in this paper. In the scheme, we merge the two heterogeneous but complementary positioning technologies on the mobile node equipped with both inertial sensors and the chirp-spread-spectrum ranging hardware. In order to NLOS identification and decrease NLOS error, a novel sight-state estimation method based on the Markov model is proposed. Besides, experiments have been carried out in real indoor NLOS environment to evaluate performance of proposed system. Experimental results indicate a remarkable performance improvement by using the proposed integrated system.
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