Bridging the gap between sensor noise modeling and sensor characterization

Jerath, Kshitij; Brennan, Sean; Lagoa, Constantino

doi:10.1016/j.measurement.2017.09.012

Cited by 47 publications

(36 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, the gyroscopic signal includes quantization error (QE), angle random walk (ARW), bias instability (BI), rate random walk (RRW), and rate ramp (RR) [111].…”

Section: B Results On Real-world Signals 1) Signals Detailsmentioning

confidence: 99%

Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising

Brzostowski

2020

IEEE Access

View full text Add to dashboard Cite

Effective signal denoising methods are essential for science and engineering. In general, denoising algorithms may be either linear or non-linear. Most of the linear ones are unable to remove the noise from the real-world measurements. More suitable methods are usually based on non-linear approaches. One of the possible algorithms to signal denoising is based on empirical mode decomposition. The typical approach to the empirical mode decomposition-based signal denoising is the partial reconstruction. More recently, a new concept inspired by the wavelet thresholding principle was proposed. The method is named the interval thresholding. In this article, we further extend the concept by the application of the sparse reconstruction to the empirical mode decomposition-based signal denoising algorithm. To this end, we state and then solve the problem of signal denoising as a regularization problem. In the article, we consider three cases, that is, three types of penalty functions. The first algorithm is combining total variation denoising with empirical mode decomposition approach. In the second one, we applied the fused LASSO Signal Approximator to design the empirical mode decomposition-based signal denoising algorithm. The third approach solves the denoising problem by applying a non-convex sparse regularization. The proposed algorithms were validated on synthetic and real-world signals. We found that the proposed methods have the ability to improve the accuracy of the signal denoising in comparison to the reference methods. Significant improvements from both the synthetic and the real-world signals were obtained for the algorithm based on non-convex sparse regularization. The presented results show that the proposed approach to signal denoising based on empirical mode decomposition algorithm and sparse regularization gives a great improvement of accuracy, and it is the promising direction of future research. INDEX TERMS non-linear signal processing, non-convex optimization, gyroscopes.

show abstract

“…In general, the gyroscopic signal includes quantization error (QE), angle random walk (ARW), bias instability (BI), rate random walk (RRW), and rate ramp (RR) [111].…”

Section: B Results On Real-world Signals 1) Signals Detailsmentioning

confidence: 99%

Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising

Brzostowski

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To simulate a colored noise, a 1/ filter was applied to the white noise and [17]. Although, sensor like gyroscope usually presents a dominant flicker noise in a frequency range of 0.1-0.001 Hz [18], in this work it is assumed that the flicker noise is dominant over the full frequency range.…”

Section: Methodsmentioning

confidence: 99%

Sensor Fusion INS/GNSS based on Fuzzy Logic Weighted Kalman Filter

Cunto¹,

Sąsiadek²

2020

International Conference of Control, Dynamic Systems, and Robotics

View full text Add to dashboard Cite

A Fuzzy Logic Adaptive Control (FLAC) is used to adjust the exponential weighting parameter of a weighted Error-State Kalman Filter (ESKF) in an INS/GNSS system. The FLAC is used to prevent the Kalman Filter (KF) from diverging or to reach to a high bound when the IMU produces colored noise. Furthermore, a matrix notation for the weighting parameter alpha is introduced and compared against the single alpha value. First, the results show the influence of a colored noise in the system, which makes the ESKF reaching a large error bound solution. The application of FLAC considering both constant and matrix alpha reduces the error boundary for the position and velocity states. However, the constant alpha leads to an inaccurate altitude, bias correction, and error covariance matrix. The matrix alpha parameter shows a final solution that improves the navigation accuracy for all states, preserving the stability of the error covariance matrix.

show abstract

“…There's a lot of disturbance in the actual engineering. [32] [33]. Next, this paper will explain how to bring the design of the levitation controller closer to the actual engineering.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Rejection Control Using a Novel Velocity Fusion Estimation Method for Levitation Control Systems

Xia

Dou

2020

IEEE Access

View full text Add to dashboard Cite

Magnetic levitation has been applied to maglev trains and magnetic suspension wind tunnel. However, there are some problems with the existing levitation control. For example, it is difficult to extract smooth velocity signals from the gap sensor with noise. The classical differentiator is susceptible to noise, which makes the levitation system sometimes vibrate. The accuracy and stability of levitation control need to be improved. The velocity fusion estimation method (VFE) is proposed to extract the velocity signal from the gap and acceleration sensor, which is theoretically derived to prove that it reduced the noise of the velocity signal. Then disturbance rejection control(DRC) is proposed that add VFE into classical levitation control. Because of the high-quality velocity signal and accelerometer's fast responsiveness, it makes DRC have many advantages. The advantages of using the proposed control structure are that it improved the control accuracy of the target gap and resisted disturbance effectively. Air-gap fluctuations in levitation systems can be reduced in transient pulse disturbance test, white noise disturbance test, and external force disturbance test when it applies the proposed control method. The effectiveness and advantages of the proposed control method are verified by the simulation and experiments.

show abstract

Bridging the gap between sensor noise modeling and sensor characterization

Cited by 47 publications

References 27 publications

Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising

Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising

Sensor Fusion INS/GNSS based on Fuzzy Logic Weighted Kalman Filter

Disturbance Rejection Control Using a Novel Velocity Fusion Estimation Method for Levitation Control Systems

Contact Info

Product

Resources

About