Wavelet de-noising for IMU alignment

El‐Sheimy, Naser; Nassar, Sameh; Noureldin, Aboelmagd

doi:10.1109/maes.2004.1365016

Cited by 140 publications

(67 citation statements)

References 4 publications

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“…It possesses of a realtime recursive structure which is different from the block processing structure of wavelet [18,25]. Moreover, its computational complexity is very low.…”

Section: A New Prefilter For Strapdown Inertial Navigation System In mentioning

confidence: 99%

“…In all open literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], the azimuth angle error variance is set as a very small value. Assumed that the initial azimuth angle error is 1ˆ, it is well accepted that the initial value of its estimated state variance should be set as ð1ˆÞ 2 .…”

Section: Azimuth Angle Error Variance Tuning For Precise Azimuth Aligmentioning

confidence: 99%

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New techniques for initial alignment of strapdown inertial navigation system

Lü

Xie

Chen

2009

Journal of the Franklin Institute

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“…It possesses of a realtime recursive structure which is different from the block processing structure of wavelet [18,25]. Moreover, its computational complexity is very low.…”

Section: A New Prefilter For Strapdown Inertial Navigation System In mentioning

confidence: 99%

Section: Azimuth Angle Error Variance Tuning For Precise Azimuth Aligmentioning

confidence: 99%

New techniques for initial alignment of strapdown inertial navigation system

Lü

Xie

Chen

2009

Journal of the Franklin Institute

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“…The MEMS inertial sensor consists of three dimensional accelerometers, gyroscopes and magnetometers. A pre-processing stage which involved wavelet denoising (Naser et al, 2004) …”

Section: Gps-aided Insmentioning

confidence: 99%

Implementation of Anfis for GPS-Aided Ins Uav Motion Sensing at Short Term GPS Outage

Lim¹,

Lim²,

Koo³

2014

Journal of Computer Science

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The recent improvement in Micro-Electro-Mechanical System (MEMS) technology has enabled the evolvement of Inertial Navigation Unit (INU) to be built on top of a low cost, small size Integrated Circuit (IC) chip. Due to the nature of the MEMS INU, its outputs are normally corrupted by the resided stochastic noise. A common practice to regulate its measurements into usable motion data is by fusing the Global Positioning System (GPS) measurement data with the MEMS INU measurement data through Kalman filter for position, velocity and orientation estimations. Such integrated system is known as GPS-aided Inertial Navigation System (INS). Note that the robustness of the GPS-aided INS relies heavily on the availability of the GPS signals. In the event of no GPS signals, the overall system will solely depend on the INU to predict the position, velocity and orientation. The prediction results will eventually drift from its true value due to the INU's resided stochastic noise. In this study, a remedy system using Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to improve the performance of the GPS-aided INS during GPS outage condition. UAV motion sensing experiment was carried out and GPS outage conditions were imposed at several locations during the UAV navigation. The motion prediction dataduring GPS outages, with and without ANFIS implementation, were compared and the results clearly show that the GPS-aided INS with ANFIS implementation achieved better performance than the GPS-aided INS without ANFIS.

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“…Hou Tieshuang [4] used wavelet analysis in the ship-radiated noise signal processing. Many Researchers analyzed the wavelet denoising method with different threshold functions, noise estimation and the wavelet decomposition levels and so on [5,6].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Dynamic Testing Data Processing Using Wavelet Analysis

Bao¹

2013

Proceedings of the Third International Conference on Control, Automation and Systems Engineering

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Abstract-The vehicle dynamic test data usually have some noise which would bring serious negative effects. In order to accurately obtain vehicle dynamic response. The test datas must be denoised using suitable signal denoising method. For this purpose, the basic principle of wavelet analysis is presented and the method of wavelet threshold denoising is used in automotive handling and stability test data processing. The denoising result shows that the wavelet denoising method is effective. And it provided a theoretical basis and practical engineering experience for vehicle handling and stability test data de-noising processing.

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Wavelet de-noising for IMU alignment

Cited by 140 publications

References 4 publications

New techniques for initial alignment of strapdown inertial navigation system

New techniques for initial alignment of strapdown inertial navigation system

Implementation of Anfis for GPS-Aided Ins Uav Motion Sensing at Short Term GPS Outage

Vehicle Dynamic Testing Data Processing Using Wavelet Analysis

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