GPS/INS enhancement using neural networks for autonomous ground vehicle applications

Kaygısız, Burak H.; Erkmen, Aydan M.; Erkmen, I.

doi:10.1109/iros.2003.1249740

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…In this evolution, a variety of new sensors, such as electronic compasses, barom-Consequently, the traditional Extended Kalman Filter (EKF) approach to multi-sensory data integration may no longer be able to properly handle the often non-Gaussian and nonlinear measurement models and more complex dynamic models. As a result, nonlinear Bayesian Filters, such as Unscented Kalman Filter (UKF) and Particle Filter (PF) recently introduced to navigation applications (see, for example, Julier and Uhlmann 1997, Wan and van der Merwe 2001, Liu and Chen 1998, Ristic et al 2004, and nontraditional approaches to sensor integration and modeling, such as Artificial Neural Networks (ANN) (Kaygisiz et al 2003, Chiang et al 2003, Wang et al 2006, Grejner-Brzezinska et al 2006c, and fuzzy logic (e.g., Simon 2003, Abdel-Hamid et al 2006 are being introduced to navigation algorithms. In the pedestrian navigation system proposed by Thienelt et al (2006), a knowledge-based component is used for outlier detection in the observation data, and quality analysis and calibration of the multi-sensor system.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

Grejner‐Brzezinska¹,

Toth²,

Moafipoor³

2007

Journal of Applied Geodesy

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The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable) 50%.

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Section: Introductionmentioning

confidence: 99%

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

Grejner‐Brzezinska¹,

Toth²,

Moafipoor³

2007

Journal of Applied Geodesy

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“…The artificial neural network (ANN) is applied to mimic the behavior of the navigation error accumulations of INS with training data; however, the error mitigation efficiency is limited when the training is insufficient. 9,10 More precise and complexed models, such as auto-regressive (AR) model, are also used in the filtering for the inertial sensor errors. Although some improvements are observed on the estimation accuracy, the randomness of sensor errors still leads quick accumulations of navigation errors.…”

Section: Introductionmentioning

confidence: 99%

A micro-electro-mechanical-system-based inertial system with rotating accelerometers and gyroscopes for land vehicle navigation

2017

International Journal of Distributed Sensor Networks

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Micro-electro-mechanical-system accelerometers and gyroscopes outputs are corrupted by significant random errors. The rotation modulation technique, which was mainly applied to ring laser gyro and fiber optical gyro, has been recently employed to compensate the micro-electro-mechanical-system inertial sensor errors in an inertial navigation system. As the fluctuating error processes in micro-electro-mechanical-system inertial sensors are difficult to be modeled or estimated, the inertial measurement unit rotating is an efficient way to automatically compensate for these random errors. This research applied the rotation modulation technique to the low-cost micro-electro-mechanical-system sensors and proposed an inertial system with rotating accelerometers and gyros for the land vehicle navigations. As previous research only studied the low-cost rotary inertial systems in the static conditions or simulation scenarios, the kinematic field tests are carried out to take the first look on the details and results of the low-cost rotary system for vehicular navigation. Accordingly, as the sensor random errors are very difficult to be dealt with, we conducted the analysis for the effect of inertial measurement unit rotation imposed on the random errors of the micro-electro-mechanical-system inertial sensors. Two different micro-electro-mechanical-system inertial measurement units are tested with a single-axis indexing table. The test results indicate the error mitigations are also related to the error characteristics of the inertial measurement unit as the navigation errors are more efficiently compensated for the micro-electro-mechanical-system inertial measurement unit that contains mainly the time-correlated noise than the one features significant white noise.

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“…Various investigations have been conducted to employ external navigation information to limit the INS error accumulation. The neural networks are applied to mimic the behavior of navigation error accumulation of INS [10,11], and the fuzzy logic is also employed to adaptively adjust the parameters of the estimation algorithm [12]. The auto-regressive model was also proposed to interpret the randomness of the MEMS-based inertial sensor errors [3,13].…”

Section: Introductionmentioning

confidence: 99%

MEMS IMU Error Mitigation Using Rotation Modulation Technique

Sun

Gao

2016

Sensors

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Micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) outputs are corrupted by significant sensor errors. The navigation errors of a MEMS-based inertial navigation system will therefore accumulate very quickly over time. This requires aiding from other sensors such as Global Navigation Satellite Systems (GNSS). However, it will still remain a significant challenge in the presence of GNSS outages, which are typically in urban canopies. This paper proposed a rotary inertial navigation system (INS) to mitigate navigation errors caused by MEMS inertial sensor errors when external aiding information is not available. A rotary INS is an inertial navigator in which the IMU is installed on a rotation platform. Application of proper rotation schemes can effectively cancel and reduce sensor errors. A rotary INS has the potential to significantly increase the time period that INS can bridge GNSS outages and make MEMS IMU possible to maintain longer autonomous navigation performance when there is no external aiding. In this research, several IMU rotation schemes (rotation about X-, Y- and Z-axes) are analyzed to mitigate the navigation errors caused by MEMS IMU sensor errors. As the IMU rotation induces additional sensor errors, a calibration process is proposed to remove the induced errors. Tests are further conducted with two MEMS IMUs installed on a tri-axial rotation table to verify the error mitigation by IMU rotations.

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GPS/INS enhancement using neural networks for autonomous ground vehicle applications

Cited by 13 publications

References 2 publications

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

A micro-electro-mechanical-system-based inertial system with rotating accelerometers and gyroscopes for land vehicle navigation

MEMS IMU Error Mitigation Using Rotation Modulation Technique

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