access icon free Adaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal

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.

Inspec keywords: signal sampling; signal denoising; light interferometry; adaptive Kalman filters; inertial navigation; fibre optic gyroscopes; covariance analysis

Other keywords: Allan variance analysis; strap down inertial navigation system; IFOG sensor; suboptimal fading factor; SINS; kernel component; random drift error; adaptive sampling strong tracking scaled unscented kalman filter algorithm; ASSTA method; state error covariance; interferometric flbre optic gyroscope; fibre optic gyroscope drift signal denoising

Subjects: Optical interferometry; Filtering methods in signal processing; Fibre optic sensors; Probability theory, stochastic processes, and statistics; Fibre optic sensors; fibre gyros; Other topics in statistics

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