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

Narasimhappa, Mundla; Sabat, Samrat L.; Nayak, Jagannath

doi:10.1049/iet-smt.2014.0001

Cited by 35 publications

(27 citation statements)

References 48 publications

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“…More complex solutions such as the extended Kalman filter (EKF) are widely applied to mitigate the signal drift problem. The EKF method is capable of eliminating a great part of the white noise influence and completely negates the drift problem, returning a signal with small error [11]. However, it requires knowledge of the machine model to predict the next state of the machine.…”

Section: Integration Driftmentioning

confidence: 99%

“…Particularly, the authors of [10] proposed an adaptive filter based on least mean square (LMS) for noise cancellation that diminishes the signal drift in electrocardiogram signals. In [11] a Kalman filter is used to cancel the drift and filter the measurement noise in fiber optic gyroscope drift signals. In [12] a Kalman filter is applied to a real-time RFID indoor positioning system to remove the position estimation drift.…”

Section: Introductionmentioning

confidence: 99%

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A New Flux Linkage Estimation with Drift Cancellation Technique for Switched Reluctance Machines

Paula

Barros

2020

Electronics

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Assessing the flux linkage is an important feature for electric machines, including switched reluctance machines. However, sensor calibration errors and noise impose a problem known as drift, which is caused by the integration process used to estimate the flux linkage. This paper proposes a flux linkage estimation with a drift cancellation technique. The technique accounts for the switched reluctance machine’s working and driving principles. Simulation and experimental results are used to validate the proposed method. Additionally, a goodness-of-fit analysis is performed to support the outcomes. A R 2 factor of 0.9823 is found between the experimentally and simulation-estimated flux linkage. The results indicate that the method works properly and may be further used in control techniques and iron losses estimation.

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Section: Integration Driftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Flux Linkage Estimation with Drift Cancellation Technique for Switched Reluctance Machines

Paula

Barros

2020

Electronics

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“…In order to overcome the above disadvantages, one solution is to introduce fading factors in the state error covariance matrix based on the residual sequence. This method is named as the strong tracking filter (STF), which was proposed by Zhou and Frank [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Strong Tracking Filtering Algorithm of Randomly Delayed Measurements for Nonlinear Systems

Yang

Gao

Liu

2015

Mathematical Problems in Engineering

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This paper focuses on the filtering problems of nonlinear discrete-time stochastic dynamic systems, such as the model simplification, noise characteristics uncertainty, initial conditions uncertainty, or system parametric variation. Under these circumstances, the measurements of system have one sampling time random delay. A new method, that is, strong tracking filtering algorithm of randomly delayed measurements (STF/RDM) for nonlinear systems based on recursive operating by analytical computation and first-order linear approximations, is proposed; a principle of extended orthogonality is presented as a criterion of designing the STF/RDM, and through the residuals between available and predicted measurements, the formula of fading factor is obtained. Under the premise of using the extended orthogonality principle, STF/RDM proposed in this paper can adjust the fading factor online via calculating the covariance of residuals, and then the gain matrices of the STF/RDM adjust in real time to enhance the performance of the proposed method. Lastly, in order to prove that the performance of STF/RDM precedes existing EKF method, the experiment of tracking maneuvering aircraft is carried out.

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“…Once the fault is successfully detected and isolated, the STF could be used to track the development of fault amplitude using its strong ability to track abrupt changes and strong robustness to model mismatch [23,24]. The augmented estimatêand its estimation error covariance matrix at can be obtained based on STF as (22) …”

Section: Fault Amplitude Estimation Based On Stfmentioning

confidence: 99%

Time-Varying Fault Diagnosis for Asynchronous Multisensor Systems Based on Augmented IMM and Strong Tracking Filtering

Xue

Jin

et al. 2018

Journal of Control Science and Engineering

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A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMMbased approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method.

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

Cited by 35 publications

References 48 publications

A New Flux Linkage Estimation with Drift Cancellation Technique for Switched Reluctance Machines

A New Flux Linkage Estimation with Drift Cancellation Technique for Switched Reluctance Machines

Strong Tracking Filtering Algorithm of Randomly Delayed Measurements for Nonlinear Systems

Time-Varying Fault Diagnosis for Asynchronous Multisensor Systems Based on Augmented IMM and Strong Tracking Filtering

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