Improvement of a Low Cost MEMS Inertial-GPS Integrated System Using Wavelet Denoising Techniques

Kang, Chang Ho; Kim, Sun Young; Park, Chan Gook

doi:10.5139/ijass.2011.12.4.371

Cited by 28 publications

(15 citation statements)

References 11 publications

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“…The calibration process has been investigated and reported by a number of researchers [ 19 , 20 , 21 , 22 ]. However, the random part can lead to the drifts and instabilities in bias or scale factor over time, which is the key component leading to the INS errors divergence [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)

Jiang

Shuai

Chen

et al. 2018

Sensors

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Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

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Section: Introductionmentioning

confidence: 99%

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)

Jiang

Shuai

Chen

et al. 2018

Sensors

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“…Nassar presented a high-order AR model and compared the disadvantages of Yule-Walker method, covariance method and Burg method for better parameter estimation (Quinchia et al 2004). There are several other wavelet-based denoising techniques that were successfully applied for improving the navigation accuracy (Kang et al 2011). The wavelet denoising technique is an effective time-frequency analysis method and is used to mitigate INS sensor stochastic errors to improve GNSS/INS integration accuracy (Noureldin et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages

et al. 2016

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This paper proposes a robust wavelet-based tightly coupled Global Positioning System (GPS)/ Beidou Navigation Satellite System (BDS)/Inertial Navigation System (INS) integration scheme aiming to improve the overall position accuracy during signal outages. A robust wavelet denoising model based on a-trimmed mean filter demonstrates its effectiveness on noise reduction and gross error elimination of inertial sensor raw data. Thereafter, a robust wavelet-based tightly coupled GPS/BDS/INS integration scheme is proposed, and GPS/BDS double-difference (DD) carrier-phase and pseudorange measurements are introduced to build a 27-state tightly coupled GPS/BDS/INS integration equation. An extended Kalman filter (EKF) has been designed for state estimation, and the inclusion of BDS enhances the satellite geometric strength of the whole navigation system. A field vehicle test indicates that the position accuracy of five simulated GPS/ BDS outages can be improved by about 7,16, and 33% for north, east, and up components with the proposed scheme compared to standard integration scheme, and gross errors have been detected and eliminated with the new integration scheme. The authors also find that the BDS phase residual is larger than GPS and satellite dependent in the tightly coupled GPS/BDS/INS integrated navigation system.

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“…Support vector machine was also used to compensate the drift of the MEMS gyroscope . The wavelet filter method was used to compensate noise by a multiscale discrete system model with multiscale decomposition . The compressed perception theory was applied to the MEMS gyroscope random noise filter in order to overcome the deficiency of the wavelet filter .…”

Section: Introductionmentioning

confidence: 99%

A new MEMS gyroscope drift suppression method for the low‐cost untwisting spin platform

Wang

Hui

2016

IEEJ Transactions Elec Engng

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The microelectromechanical system (MEMS) gyroscope provides a new method to design a low-cost untwisting spin platform to be used in a single-axis, stable strap-down inertial navigation system. However, the MEMS gyroscope's drift reduces the effectiveness of the closed-loop feedback control. Thus, a new method of drift suppression is proposed in this paper based on phase space reconstruction in order to improve the platform's performance. The feasibility of the MEMS gyroscope's drift suppression is analyzed using linear decomposition based on phase space. The system drift is estimated by phase space reconstruction. The optimal embedding dimension is found through a grid search. The number of dimensions for dimension reduction analysis is selected according to the minimum eigenvalue. The mapping from the high-dimensional phase space onto the low-dimensional phase space is obtained by minimizing the variance. A Kalman filter is used to compensate the residual sequence further. The proposed method is applied to an untwisting spin platform based on the MEMS gyroscope L3G4200D. The experimental results show that it can reduce the platform drift rate effectively.

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Improvement of a Low Cost MEMS Inertial-GPS Integrated System Using Wavelet Denoising Techniques

Cited by 28 publications

References 11 publications

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)

Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages

A new MEMS gyroscope drift suppression method for the low‐cost untwisting spin platform

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