Adaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal. Issue 3 (1st May 2015)

• Record Type: Journal Article
• Authors: Narasimhappa, Mundla; Sabat, Samrat L.; Nayak, Jagannath
• Is Part Of: IET science, measurement & technology. Volume 9:Issue 3(2015)
• Page Start: 241
• Page End: 249
• Publication Date: 2015-05-01
• Subjects: signal sampling -- adaptive Kalman filters -- signal denoising -- fibre optic gyroscopes -- light interferometry -- inertial navigation -- covariance analysis; adaptive sampling strong tracking scaled unscented kalman filter algorithm -- fibre optic gyroscope drift signal denoising -- interferometric flbre optic gyroscope -- kernel component -- strap down inertial navigation system -- SINS -- random drift error -- IFOG sensor -- state error covariance -- suboptimal fading factor -- ASSTA method -- Allan variance analysis; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2014.0001
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : 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.