Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

Noureldin, Aboelmagd; Karamat, Tashfeen B.; Eberts, M.D.; El-Shafie, Ahmed

doi:10.1109/tvt.2008.926076

Cited by 321 publications

(179 citation statements)

References 21 publications

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“…By completion of this task, KF provides optimal least mean variance estimation of searched states (Kalman 1960;Welch, Bishop 2006). In case of INS/GPS integration there are usually errors of values calculated by the INS system (Solimeno 2007;Noureldin et al 2009Noureldin et al , 2012Zhao 2011;Groves 2013;Grewal et al 2013). Kalman filtering also provides information considering accuracy of performed calculations to determine the impact of each observable on the final result.…”

Section: Basic Concept Of Kalman Filteringmentioning

confidence: 99%

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Concept of INS/GPS Integration Algorithm Designed for MEMS Based Navigation Platform

Tomaszewski

2017

Proccedings of 10th International Conference "Environmental Engineering"

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Abstract. Nowadays, along with the advancement of technology one can notice the rapid development of various types of navigation systems. So far, the most popular satellite navigation, is now supported by positioning results calculated with the use of other measurement system. The method and manner of integration will depend directly on the destination of developed system. To increase the frequency of readings and improve the operation of outdoor navigation systems, one can support satellite navigation systems (GPS, GLONASS ect.) with inertial navigation (INS/GPS systems). For the last two decades, due to the miniaturization in electronics it is possible to build GPS/INS systems with the use of low cost MEMS devices. The following study presents the concept of Kalman filter based integration algorithm designed for cheap micro -electronic navigation platform. The tests of considered algorithm were made with the use of MEMS smartphone sensors. Subsequently the results of the study were analyzed to determine the accuracy of featured algorithm.

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Section: Basic Concept Of Kalman Filteringmentioning

confidence: 99%

“…Expensive INS are used for many years in marine, aviation and missile navigation. Nowadays, through the use of MEMS technology (Micro Electro-Mechanical Systems), they have become available for a large number of users (Solimeno 2007;Syed et al 2007;Zhao 2011;Georgy et al 2011;Noureldin et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Concept of INS/GPS Integration Algorithm Designed for MEMS Based Navigation Platform

Tomaszewski

2017

Proccedings of 10th International Conference "Environmental Engineering"

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“…In addition, the FIS are capable to choose an optimal MF under certain convenient criteria meaningful to a specific application. The deterministic output of FIS and its performance depend on the effective fuzzy rules, the considered defuzzification process and the reliability of the MF values [19,20].…”

Section: Fuzzy Inference Systems (Fiss)mentioning

confidence: 99%

A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Navidi

Landry

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays, Global Positioning System (GPS) receivers are aided by some complementary radio navigation systems and Inertial Navigation Systems (INS) to obtain more accuracy and robustness in land vehicular navigation. Extended Kalman Filter (EKF) is an acceptable conventional method to estimate the position, the velocity, and the attitude of the navigation system when INS measurements are fused with GPS data. However, the usage of the low-cost Inertial Measurement Units (IMUs) based on the MicroElectro-Mechanical Systems (MEMS), for the land navigation systems, reduces the precision and stability of the navigation system due to their inherent errors. The main goal of this paper is to provide a new model for fusing low-cost IMU and GPS measurements. The proposed model is based on EKF aided by Fuzzy Inference Systems (FIS) as a promising method to solve the mentioned problems. This model considers the parameters of the measurement noise to adjust the measurement and noise process covariance. The simulation results show the efficiency of the proposed method to reduce the navigation system errors compared with EKF.

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“…In land vehicle applications, Caron et al [11] and Noureldin et al [12] propose machine learning techniques like neural networks, which introduce context variables and errors modelling for each sensor. Authors conclude that with an adequate modelling an accuracy…”

Section: Introductionmentioning

confidence: 99%

Step Characterization using Sensor Information Fusion and Machine Learning

Anacleto¹,

Figueiredo²,

Almeida³

et al. 2015

IJIMAI

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-A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, lowcost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person's body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications.

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Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

Cited by 321 publications

References 21 publications

Concept of INS/GPS Integration Algorithm Designed for MEMS Based Navigation Platform

Concept of INS/GPS Integration Algorithm Designed for MEMS Based Navigation Platform

A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Step Characterization using Sensor Information Fusion and Machine Learning

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