Adopting Neural Networks in GNSS-IMU integration: A Preliminary study

Abstract

Most autonomous vehicle navigation systems rely on Global Navigation Satellite System (GNSS) as a primary positioning sensor. However, a standalone GNSS receiver may not be able to meet the required positioning performance in aspects of position accuracy, robustness against signal blockages or signal reflections, and position output rates. Therefore, an integrated navigation system with an Inertial Measurement Unit (IMU) and GNSS receiver is often used to provide a continuous, reliable, high-bandwidth navigation solution. In general, Kalman filter is often used to integrate IMU measurements and GNSS output with predefined characteristic models under stochastic assumptions. If there is a discrepancy between the predefined model and actual sensor behavior or when proper correction on IMU measurements is not possible due to GNSS outages, the positioning performance of the filter will be degraded. For the application having a stringent positioning performance such as autonomous vehicle navigation, a more sophisticated sensor model or methods should be employed. A Recurrent Neural Network (RNN) - Long Short-Term Memory (LSTM) network has been found to have strengths in making a prediction with time-series data and has been applied for various navigation problems from the prior research. Therefore, in this paper, to reflect the non-linearity and time-correlated characteristics of the actual IMU sensor measurement, an RNN-LSTM network is employed for the GNSS-IMU integration. The paper investigates RNN-LSTM based GNSS-IMU integration methods through simulation and experiment and the results showed that the proposed RNN-LSTM network can provide relatively stable positioning performance even in a situation where a GNSS signal can not be received for 200 seconds.