Intelligently tuned wavelet parameters for GPS/INS error estimation

Hasan, Ahmed Mudheher; Samsudin, Khairulmizam; Ramli, Abd Rahman

doi:10.1007/s11633-011-0598-9

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…Like the entropy features of phase-1, the average of the 2 significance indices (uniqueness and discrimination capability) is not calculated as the significance lies in the complementary sub-bands. Thus, the four feature matrices (energy, entropy feature matrices of the statistical and biological-ROI phases, as listed in Section 3.1) are feature reduced using (11) and (12). The number of features selected out of these feature matrices are 23, 17, 3, 39, respectively.…”

Section: Feature Reductionmentioning

confidence: 99%

“…Totally, 49% of the entropy features of the biological-ROI phase are significant giving an insight that the entropy is significant in the biological-ROI phase. The entropy difference as in (11) varies significantly between the detailed coefficient sub-bands of the same image. In other word, the entropy features of the detailed co-efficient sub-bands are unique.…”

Section: Analysis About the Entropy Feature Matrix Of The Biological-mentioning

confidence: 99%

“…Based on the p-value, "db3" wavelet is chosen from a set of wavelet bases. Further, the difference between two adjacent orders of the same wavelet function is insignificant as they are analogous to each other [11] .…”

Section: Introductionmentioning

confidence: 99%

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Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses

Raja¹,

Gangatharan²

2015

Int. J. Autom. Comput.

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Abstract:The most common reason for blindness among human beings is Glaucoma. The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision. A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper. This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition (WPD). Decomposition is done with "db3" wavelet function and the images are decomposed up to level-3 producing 84 sub-bands. Two features, the energy and the entropy are calculated for each sub-band producing two feature matrices (158 images × 84 features). The above step is purely a statistical measure based on WPD. To enhance the diagnostic accuracy, the second phase considers the structural (biological) region of interest (ROI) in the image and then extracts the same features. It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI. Interestingly, the detailed coefficient sub-bands (prominent edges) show more significance in the biological-ROI phase. Apart from enhancing the diagnostic accuracy by feature reduction, the paper intends to mark the significance indices, uniqueness and discrimination capability of the significant features (sub-bands) in both the phases. Then, the crisp inputs are fed to the classifier ANN. Finally, from the significant features of the biological-ROI feature matrices, the accuracy is raised to 85% which is notable than the accuracy of 79% achieved without considering the ROI.

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Section: Feature Reductionmentioning

confidence: 99%

Section: Analysis About the Entropy Feature Matrix Of The Biological-mentioning

confidence: 99%

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Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses

Raja¹,

Gangatharan²

2015

Int. J. Autom. Comput.

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“…Also, the results of comparison for various wavelet thresholding selections with different level of decomposition for each GPS and INS component where exposed in [9]. 44 The research of the wavelet method upon the INS/GPS systems led to different tandems such as wavelet multiresolution analysis algorithm -based on genetic algorithm ( [10]), or wavelet multi-resolution analysis and artificial neural networks ( [11]).…”

Section: IIImentioning

confidence: 99%

Assistive inertial 3D positioning MEMS system

Adochiei

Grigorie

Adochiei

et al. 2015

2015 E-Health and Bioengineering Conference (EHB)

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This paper describes the implementation and experimental validation of a new 3D miniaturized inertial navigator developed for human indoor and outdoor monitoring applications. Accuracy is a major concern, especially during GPS outages and indoor applications. In order to increase the inertial navigation system (INS) standalone operation period with acceptable accuracy during the GPS absence, signals received from sensors are filtered for reducing positioning errors. For validation, data achieved from the proposed navigator was compared with data achieved from a GPS/INS integrated navigator, both devices being used as remote systems. Unlike in our previous research, the case of the bi-dimensional model, when we managed to develop an optimizing tuning method for achieving the optimum level of decomposition of the Wavelet filter for data achieved from two channels, the attitude channel and the position channel, but not simultaneously, we are currently extending our research aiming to tune simultaneously data achieved from six detection channels, three accelerometers and three gyros.

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“…For a detailed definition of all the CONTACT Elder M. Hemerly hemerly@ita.br parameters in this stochastic model, see IEEEStd 952-1997IEEEStd 952- (1998, which also recommends the Allan Variance method for experimentally obtaining these parameters. This model can then be used, for instance, to implement INS-based navigators, such as in Hasan, Samsudin, and Ramli (2011) and Wang, Wang, Liang, Zhang, and Zhou (2013). A comprehensive example of the classical approach for stochastic error modelling, based on Allan Variance analysis, is Petkov and Slavov (2010).…”

Section: Introductionmentioning

confidence: 99%

MEMS IMU stochastic error modelling

Hemerly

2016

Systems Science & Control Engineering

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Low cost inertial measurement units (IMU) are comprised of micro-electro-mechanical systems (MEMS) gyroscopes and accelerometers. These inertial sensors have high random noise and time varying bias. Hence, an accurate stochastic error model is necessary to predict performance and also to implement an attitude and heading reference system or a navigator. The parameters of this stochastic model are classically obtained via Allan Variance analysis. In this paper, two modern approaches, based on autoregressive moving average model and Kalman Filter, respectively, are investigated and their performances are unveiled via sensitivity analysis, realistic simulations with typical MEMS parameters and also with experimental data. Basic equations for the estimation problem are developed and the solution sensitivity to data and parameters is discussed. It is shown that the deficiency with the online methods can be traced back to the difficulty in estimating the parameters B (bias instability) and K (rate random walk for gyros, acceleration random walk for accelerometers) separately under high measurement noise N (angular random walk for gyros, velocity random walk for accelerometers), which is typical of MEMS IMU.

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Intelligently tuned wavelet parameters for GPS/INS error estimation

Cited by 13 publications

References 18 publications

Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses

Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses

Assistive inertial 3D positioning MEMS system

MEMS IMU stochastic error modelling

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