A Novel Scalar Adaptive Filter for Mitigating the Cycle Slip

Abstract

This paper presents a novel scalar adaptive filter, which is reformulated by additional acceleration term. The filter continuously estimates the measurement noise covariance, the velocity error covariance and the acceleration error covariance. For estimating three covariances, we use the innovation method for the measurement noise covariance and the least square method for other covariances. In order to verify the proposed filter performance, we compared with Kalman filter and the conventional scalar adaptive filer. We make indoor test bed using the ultrasonic sensors. Experimental results show that the proposed filter has better position accuracy than the traditional scalar adaptive filter and Kalman filter.