Adaptive Fingerprinting in Multi-Sensor Fusion for Accurate Indoor Tracking

Abstract

Indoor Localization and Tracking have become an attractive research topic because of the wide range of potential applications. These applications are highly demanding in terms of estimation accuracy and rise a challenge due to the complexity of the scenarios modeled. Approaches for these topics are mainly based on either deterministic or probabilistic methods, such as Kalman or Particles Filter. These techniques are improved by fusing information from different sources, such as wireless or optical sensors. In this paper, a novel MUlti-sensor Fusion using adaptive fingerprint (MUFAF) algorithm is presented and compared with several multi-sensor indoor localization and tracking methods. MUFAF is mainly divided in four phases: first, a target position estimation (TPE) process is performed by every sensor; second, a target tracking process stage; third, a multi-sensor fusion combines the sensor information; and finally, an adaptive fingerprint update (AFU) is applied. For TPE, a complete environment characterization in combination with a Kernel density estimation technique is employed to obtain object position. A Modified Kalman Filter is applied to TPE output in order to smooth target routes and avoid outliers effect. Moreover, two fusion methods are described in this paper: track-to-track fusion and Kalman sensor group fusion. Finally, AFU will endow the algorithm with responsiveness to environment changes by using Kriging interpolation to update the scenario fingerprint. MUFAF is implemented and compared in a test bed showing that it provides a significant improvement in estimation accuracy and long-term adaptivity to condition changes.