A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages
Global Positioning System (GPS) and Inertial Navigation System (INS) are two salient technologies delivering a vehicle?s position, velocity and attitude parameters for navigation. The standalone GPS undergoes signal outages (in forest area) and multipath effects (in urban areas), whereas the standalone INS solution accuracy deteriorates with time. To overcome the limitations of standalone GPS and INS, an integrated INS/GPS system is required for continuous, accurate, and reliable navigation solution. This paper proposes a hybrid method of Principal Component Regression (PCR) and Random Forest Regression (RFR) for INS and GPS data fusion. Traditional filter based fusion techniques like Kalman filter (KF), linearized KF, and particle filter (PF) have deficiencies related to sensor error modeling, immunity to noise, and computational load. Artificial Neural Networks (ANN) are introduced to overcome these limitations, but they show poor generalization capability in the presence of noise and suffer from over fitting. Recently, RFR is proposed as an efficient fusion technique due to its flexibility to handle non-linear inputoutput relationships and overcome the over fitting problems. The limitation with RFR is that it cannot handle both linear and non-linear inputoutput relationships effectively Hence, the paper introduces PCR, which can model linear inputoutput relationships. Thus, a hybrid approach of RFR and PCR provides enhanced modeling of inputoutput relationships leading to increased prediction accuracy and thereby improved integrated INS/GPS system performance. The results are validated through five simulated GPS outages taken on real field test data. The proposed model showed a maximum of 45.06% decrement in positional error when compared to RFR.
principal component regression random forest regression global positioning system inertial navigation system