A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages

Neurocomputing

Volumn 166, Issue , 2015, Pages 185-192

  Adusumilli, Srujana ^a   Bhatt, Deepak ^a   Wang, Hong ^b   Devabhaktuni, Vijay ^a   Bhattacharya, Prabir ^c  

^a UNIVERSITY OF TOLEDO   (United States)

^b MEDICAL COLLEGE OF OHIO   (United States)

^c 705 Engineering Research Center   (United States)

Author keywords

Global Positioning System; Inertial Navigation System; Principal Component Regression; Random Forest Regression

Indexed keywords

AIR NAVIGATION; DECISION TREES; GLOBAL POSITIONING SYSTEM; KALMAN FILTERS; NAVIGATION; RANDOM FORESTS; SENSOR DATA FUSION;

ATTITUDE PARAMETER; COMPUTATIONAL LOADS; GENERALIZATION CAPABILITY; INERTIAL NAVIGATION SYSTEMS (INS); NAVIGATION SOLUTION; OVER FITTING PROBLEM; PREDICTION ACCURACY; PRINCIPAL COMPONENT REGRESSION;

INERTIAL NAVIGATION SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CONTROLLED STUDY; FILTER; GLOBAL POSITIONING SYSTEM; INERTIAL NAVIGATION SYSTEM; INFORMATION PROCESSING; KALMAN FILTER; LINEARIZED KALMAN FILTER; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; NONLINEAR SYSTEM; PARTICLE FILTER; PREDICTION; PRINCIPAL COMPONENT REGRESSION; PRIORITY JOURNAL; RANDOM FOREST REGRESSION; REGRESSION ANALYSIS; ROOT MEAN SQUARE ERROR; STATISTICAL MODEL; STATISTICAL PARAMETERS;

EID: 84931560585     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.03.080     Document Type: Article

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