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A combined extended finite impulse response (EFIR) and Kalman (EFIR/Kalman) algorithm is proposed for mobile robot localization via triangulation. A distinctive advantage of the EFIR algorithm is that it completely ignores the noise statistics which are typically not well known to the engineer. Instead, it requires an optimal averaging interval of Nopt points. To run this algorithm, several initial Kalman estimates are used for the roughly set noise covariances. We consider a mobile robot travelling on an indoor floorspace and localized via triangulation with three nodes in a view. We show that the EFIR/Kalman filter is more accurate than the extended Kalman filter under the uncertain noise statistics and initial state.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. ABSTRACT This paper discusses two algorithms of extended unbiased FIR (EFIR) filtering of nonlinear discrete-time state-space models used in tracking and state estimation. The basic algorithm employs the extended nonlinear state and observation equations. The modified algorithm utilizes the nonlinear-tolinear conversion of the observation equation which is provided using a batch EFIR filter having small memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness against temporary model uncertainties. These algorithms require an optimal horizon in order to minimize the mean square error. Applications are given for robot indoor selflocalization utilizing radio frequency identification tags. Permanent repository link

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