Open AccessAdaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal
Author(s) -
Narasimhappa Mundla,
Sabat Samrat L.,
Nayak Jagannath
Publication year - 2015
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2014.0001
Subject(s) - kalman filter , gyroscope , allan variance , signal (programming language) , vibrating structure gyroscope , control theory (sociology) , noise (video) , noise reduction , computer science , algorithm , fibre optic gyroscope , artificial intelligence , engineering , mathematics , standard deviation , statistics , control (management) , programming language , aerospace engineering , image (mathematics)
The interferometric fibre optic gyroscope (IFOG) is a kernel component of strap down inertial navigation system (SINS) for providing angular rotation of any moving object. The behaviour of SINS degrades because of noise and random drift errors of the IFOG sensor. This study proposes a hybrid of adaptive sampling strong tracking algorithm (ASSTA) and scaled unscented Kalman filter algorithm for denoising the IFOG signal. In this algorithm, the state error covariance ( P ) is updated by using a suboptimal fading factor based on the innovation sequence followed by the ASSTA method. The proposed algorithm is applied for denoising the IFOG signal under static and dynamic environment to crush the random drift errors and noises. Allan variance analysis is used for analysing the efficiency of algorithms. Simulation results depict that the suggested algorithm is suitable for reducing drifts of the gyro signal.