Jagannath Nayak

Fiber-optic gyroscopes: from design to production [Invited]

Fiber-optic gyroscopes (FOGs) represent an important development in the field of inertial sensors and are now considered an alternative technology to mechanical and ring laser gyroscopes for inertial navigation and control applications. The past 30 years of research and development around the world have established the FOG as a critical sensor for high-accuracy inertial navigation systems. In this paper, specifications, system configurations, and error and sensitivity analysis for different types of FOGs, including critical technology, are presented. This paper addresses the design and production of a different FOG that fulfills requirements such as small size, low production cost, low power consumption, and a broad spectrum of applications.

A modified Sage-Husa adaptive Kalman filter for denoising Fiber Optic Gyroscope signal

Fiber Optic Gyroscope (FOG) is a key component in Inertial Navigation System. The performance of FOG degrades due to different types of random errors in the measured signal. Although Kalman filter and its variants like Sage-Husa Kalman filters are being used to denoise the Gyroscope signal the performance of Kalman filter is limited by the initial values of measurement and process noise covariance matrix, and transition matrix. To address this problem, this paper uses modified Sage-Husa adaptive Kalman filter to denoise the FOG signal. In this work, the random error of fiber optic gyroscope is modeled using a first order auto regressive (AR) model and used the coefficients of the model to initialize the transition matrix of Sage-Husa Adaptive Kalman filter. Allan variance analysis is used to quantify the random errors of the measured and denoised signal. The performance of proposed algorithm is compared with conventional Kalman filter and the simulation results show that the modified SageHusa adaptive Kalman filter (SHAKF) algorithm outperforms the conventional Kalman filter technique while denoising FOG signal.

Characterization of Fiber Optics Gyro and Noise Compensation Using Discrete Wavelet Transform

This paper presents quantification of different types of random errors present in the Fiber Optics Gyroscope (FOG) measured data using Allan Variance analysis and denoising of the measured data using Discrete Wavelet Transform (DWT). Allan Variance analysis is performed before and after denoising the measured data. The experimental result shows that after denoising the angle random walk is reduced and therefore sensitivity of FOG is increased.

Design and implementation of a realtime co-processor for denoising Fiber Optic Gyroscope signal

Abstract The amount of noise present in the Fiber Optic Gyroscope (FOG) signal limits its applications and has a negative impact on navigation system. Existing algorithms such as Discrete Wavelet Transform (DWT), Kalman Filter (KF) denoise the FOG signal under static environment, however denoising fails in dynamic environment. Therefore in this paper an Adaptive Moving Average Dual Mode Kalman Filter (AMADMKF) is developed for denoising the FOG signal under both the static and dynamic environments. Performance of the proposed algorithm is compared with DWT and KF techniques. Further, a hardware Intellectual Property (IP) of the algorithm is developed for System on Chip (SoC) implementation using Xilinx Virtex-5 Field Programmable Gate Array (Virtex-5FX70T-1136). The developed IP is interfaced as a Co-processor/ Auxiliary Processing Unit (APU) with the PowerPC (PPC440) embedded processor of the FPGA. It is proved that the proposed system is an efficient solution for denoising the FOG signal in real-time environment. Hardware acceleration of developed Co-processor is 65× with respect to its equivalent software implementation of AMADMKF algorithm in the PPC440 embedded processor.

Fiber-Optic Gyroscope Signal Denoising Using an Adaptive Robust Kalman Filter

Random noise is an important issue in interferometric fiber-optic gyroscope (IFOG). In this paper, an adaptive robust Kalman filter (KF) and a variant of this are applied to minimize the random noise in IFOG. In the variant of the adaptive robust KF, the measurement noise covariance matrix is adapted using the weighted covariance of the innovation sequence. The suitability of both the algorithms is studied for denoising the IFOG signal under static and maneuvering conditions. In the static case, the Allan variance analysis and the conventional variance are used as the performance indicators to determine the efficiency of the algorithm. In the maneuvering case, root mean-squared error is considered as the performance indicator. The performance of both the algorithms is compared with the conventional KF, innovation-based adaptive estimation adaptive KF, and for minimizing random noise. The experimental results reveal that both the algorithms are competitive for denoising the IFOG signal.

An improved adaptive Kalman filter for denoising fiber optic gyro drift signal

In this paper, an innovation based adaptive estimation Kalman filter (IAE-AKF) with double transitive factors is proposed for denoising the fiber optic gyroscope (FOG) signal. In this algorithm, double transitive adaptive factors are described in two stages. The transitive factor is introduced into the predicted state vector equation in stage one, where as in second stage, adaptive factor is scaled with measurement noise covariance matrix (R). These adaptive factors are developed based on the innovation sequence in adaptive Kalman filter. The predicted state error and measurement noise covariance matrix are updated by the double transitive adaptive factor in the process of iteration in stage one and two respectively. This algorithms is applied for denoising FOG signal in both static and dynamic conditions. The performance of proposed algorithm is compared with Conventional Kalman filter (CKF) and AKF with transitive factor. The precision improvement of FOG is calculated by variance and standard deviation, the predicted results revealed that the proposed algorithm is an efficient algorithm in drift denoising of FOG signal. In dynamic condition, the mean squared error (MSE) and root MSE (RMSE) values are calculated before and after denoising of FOG signal using proposed algorithm.

System on Chip Implementation of Adaptive Moving Average Based Multiple-Model Kalman Filter for Denoising Fiber Optic Gyroscope Signal

This paper proposes a combination of adaptive moving average process with multiple model kalman filter to efficiently denoise a digital Fiber Optic Gyroscope (FOG) signal. This algorithm has two phases i) Identification of transition of signal in a single frame of the signal ii) Filter the signal using a multiple model kalman filter. The transition locations are identified by comparing sample variance with a threshold value. Two different kalman filters are used to denoise the signal, one in the vicinity of transition region and other for non transition region. The performance of the algorithm is compared with adaptive moving average filter, standard kalman filter, standard multiple model kalman filter. Simulation results reveal that the proposed adaptive moving average based multiple model kalman filter (AMAMMKF) efficiently denoises the signal both in the transition and non-transition region. This paper also focuses on the system on chip (SoC) implementation of the proposed AMAMMKF algorithm in Virtex 5 FX70T1136-1 field programmable gate array (FPGA).

System on chip implementation of 1-D wavelet transform based denoising of fiber optic gyroscope signal on FPGA

This paper presents system on chip (SoC) implementation of 1-dimensional discrete wavelet transform (DWT) for real time denoising of fiber optic gyroscope signal (FOG) in Field Programmable Gate Array (FPGA). The Hardware DWT IP is designed by using Distributed arithmetic filters. The design is carried out in Xilinx System generator for DSP and is integrated with Microblaze embedded system for SoC implementation. Simulation was performed on the Virtex5-FX70T -1136 platform for both static and dynamic gyro signal datasets. The results show that the algorithmic (floating point) results match with hardware (fixed point) results. The resource utilization is 60% in Virtex-5FX70T and the maximum frequency of operation is 180 MHz.

Parameter optimization analysis to minimize the polarization error in a localized thermal tunable fiber ring resonator gyro

The accuracy of the resonant frequency servo loop is a major concern for the high-performance operation of a resonant fiber optic gyro. For instance, a bias error as large as tens or even hundreds of degrees/hour has been observed at the demodulated output of the resonant frequency servo loop. The traditional frequency servo mechanism is not an efficient tool to address this problem. In our previous work, we proposed a novel method to minimize the laser frequency noise to the level of the shot noise by refractive index modulation by a thermally tunable resonator. In this paper, we performed the parameter optimization for the resonator coil, multifunction integrated-optics chip, and couplers by the transition matrix using the Jones matrix methodology to minimize the polarization error. With the optimized parameter values, we achieved the bias value of the resonator fiber optic gyro to 1.924°/h.

An improved adaptive square root unscented Kalman filter for denoising IFOG signal

An interferometric fiber optic Gyroscope (IFOG) is a core component in the inertial navigation system (INS), and used to measure the rotation rate of an object based on Sagnac principle. The output of IFOG suffers with noise and random drift errors, due to the variation and fluctuations of the ambient temperature during the operation time. Random drift error is the main source of error and it degrades the IFOG accuracy. To improve the precision of IFOG, the stochastic drift error models and noise compensation methods are required to suppress these errors. In this paper, the residual based an adaptive square root unscented Kalman filter (ASRUKF) is developed for denoising the IFOG signal. In this algorithm, the Kalman gain is adapted by using window average method and followed by covariance matching technique based on residual sequence. The proposed algorithm is utilized for IFOG test signal under static and dynamic environment. Allan variance (AV) analysis used to analyze and quantify the noise sources of IFOG sensor. In static and maneuvering condition, the performance improvement of proposed algorithm is indicated by the minimum values of variance and root mean square error (RMSE). A simulation result reveals that the proposed algorithm is a valid solution for drift denoising the IFOG signal as compared to Unscented Kalman filter (UKF).