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

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PROJECT TITLE :

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

ABSTRACT:

The interferometric fibre optic gyroscope (IFOG) could be a kernel element of strap down inertial navigation system (SINS) for providing angular rotation of any moving object. The behaviour of SINS degrades as a result of 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 employing a suboptimal fading issue primarily based on the innovation sequence followed by the ASSTA methodology. The proposed algorithm is applied for denoising the IFOG signal underneath static and dynamic setting to crush the random drift errors and noises. Allan variance analysis is employed for analysing the potency of algorithms. Simulation results depict that the recommended algorithm is appropriate for reducing drifts of the gyro signal.


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Adaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal - 4.7 out of 5 based on 68 votes

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