Adaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal
Adaptive sampling strong tracking scaled unscented Kalman filter for denoising the fibre optic gyroscope drift signal
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DOI：
10.1049/iet-smt.2014.0001
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发表时间：
2015-05-01
影响因子：
1.4
通讯作者：
Nayak, Jagannath
中科院分区：
文献类型：
--
作者：
Narasimhappa, Mundla;Sabat, Samrat L.;Nayak, Jagannath
干涉光纤陀螺仪（IFOG）是捷联惯性导航系统（SINS）的核心组件，用于提供任何运动物体的角旋转。由于 IFOG 传感器的噪声和随机漂移误差，SINS 的性能会下降。本研究提出了一种自适应采样强跟踪算法 (ASSTA) 和缩放无迹卡尔曼滤波器算法的混合体，用于对 IFOG 信号进行去噪。在此算法中，状态误差协方差 (P) 通过使用基于创新序列的次优衰落因子进行更新，随后采用 ASSTA 方法。该算法应用于静态和动态环境下的IFOG信号去噪，以消除随机漂移误差和噪声。 Allan方差分析用于分析算法的效率。仿真结果表明，所提出的算法适用于减少陀螺仪信号的漂移。
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.