Toward Robust Indoor Localization Based on Bayesian Filter Using Chirp-Spread-Spectrum Ranging

It is a challenging problem to realize robust localization in complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve this problem, a localization algorithm under the Bayesian framework is proposed in this paper. We adopt the 802.15.4a chirp-spread-spectrum ranging hardware to measure the distances between the mobile node and the anchor nodes, and realize the location estimation by incorporating the range measurements into the localization algorithm. We propose a novel joint-state estimation localization algorithm which adopts a Markov model for NLOS state estimation and a particle filter for location state estimation. For utilizing the positive effect of the NLOS measurements while restraining their negative effect, we present a scheme to build the feasible region of the particles based on the NLOS and line-of-sight (LOS) measurements and calculate the particle weight based only on the LOS measurements. The results of the indoor experiment demonstrate the effectiveness of our approach.