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Development of Statistical Learning Techniques for INS and GPS Data Fusion

Adusumilli, Srujana
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1398772813

Abstract Details

2014, Master of Science, University of Toledo, Electrical Engineering.
Global Positioning System (GPS) and Inertial Navigation System (INS) are two salient technologies delivering vehicles position, velocity, and attitude parameters for land vehicle navigation. GPS provides absolute and accurate navigation parameters over extended periods of time. However, standalone GPS performance deteriorates in certain scenarios such as, when a vehicle passes through urban areas or through forests leading to satellite signal blockages and multipath effects. Whereas, INS is a self-contained navigation technology, capable of providing navigation solution by continuously measuring linear accelerations and angular velocities in three orthogonal directions. However, depending upon INS grade, their standalone accuracy varies, due to several reasons like sensor errors, scale-factor errors, noises, and drifts. Low-cost INS consisting of MEMS sensors are being used practically due to several advantages. For instance, they are cost-effective, small in size, and light in weight. Thus, to overcome the limitations of standalone GPS and INS, an integrated INS/GPS system is required for continuous, accurate, and reliable navigation solution. In an integrated system, GPS aids INS in its error modeling process thereby improving its long-term accuracy. On the other hand, INS bridges GPS gaps and assists in signal acquisition and reacquisition thus reducing the time and search domain required for detecting and correcting GPS cycle slips. Thus for an improved, reliable, and continuous navigation, their synergistic combination is preferred while simultaneously overcoming the individual unit drawbacks. This thesis aims at developing novel statistical learning algorithms, namely Random Forest Regression, hybrid of Principal Component Regression and Random Forest Regression, and Quantile Regression Forests, for INS and GPS data fusion. The performance of the proposed techniques is evaluated using real field test data. The test results demonstrated the improved positioning accuracy and reduced positional drift in comparison to existing techniques during GPS outages. Through experimental demonstration, the Quantile Regression Forests has shown improved performance by providing a maximum of 87% improvement in prediction accuracy in comparison to conventional Artificial Neural Networks.
Hong Wang (Committee Chair)
Vijay Devabhaktuni (Committee Co-Chair)
Mansoor Alam (Committee Member)
Weiqing Sun (Committee Member)
Nikolaidis Efstratios (Committee Member)
Electrical Engineering

Recommended Citations

Citations

  • Adusumilli, S. (2014). Development of Statistical Learning Techniques for INS and GPS Data Fusion [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1398772813

    APA Style (7th edition)

  • Adusumilli, Srujana. Development of Statistical Learning Techniques for INS and GPS Data Fusion. 2014. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1398772813.

    MLA Style (8th edition)

  • Adusumilli, Srujana. "Development of Statistical Learning Techniques for INS and GPS Data Fusion." Master's thesis, University of Toledo, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1398772813

    Chicago Manual of Style (17th edition)

toledo1398772813
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This open access ETD is published by University of Toledo and OhioLINK.