A comparison of methods for online lever arm estimation in GPS/INS integration

A comparison of neural network, state augmentation, and multiple model-based approaches to online location of inertial sensors on a vehicle is presented that exploits dual-antenna carrier-phase-differential GNSS. The best technique among these is shown to yield a significant improvement on a priori calibration with a short window of data. Estimation of Inertial Measurement Unit (IMU) parameters is a mature field, with state augmentation being a strong favorite for practical implementation, to the potential detriment of other approaches. A simple modification of the standard state augmentation technique for determining IMU location is presented that determines which model of an enumerated set best fits the measurements of this IMU. A neural network is also trained on batches of IMU and GNSS data to identify the lever arm of the IMU. A comparison of these techniques is performed and it is demonstrated on simulated data that state augmentation outperforms these other methods.