A low-cost INS/GPS integration methodology based on random forest regression

Authors:
Srujana Adusumilli;Deepak Bhatt;Hong Wang;Prabir Bhattacharya;Vijay Devabhaktuni
Affiliations:
EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States;EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States;ET Department, University of Toledo, MS 402, 2801 W. Bancroft St., Toledo, OH 43606, United States;School of Computing Sciences and Informatics, University of Cincinnati, Cincinnati, OH 45221, United States;EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise. In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24-56%.