An INS/GNSS fusion architecture in GNSS denied environment using gated recurrent unit

Abstract

One of the most used Position, Navigation and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit line-of-sight and reduce satellite availability to receivers. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as transportation of organs which are time-sensitive. Therefore, higher accuracy for position and velocity information is required. This paper investigates the use of Gated Recurrent Units (GRU) as a suitable technique that can memorize previous information in conjunction with the inputs (consisting of attitude, change in attitude, and change in velocity) to reduce position and velocity error when GNSS is not available. The fusion approach is developed and tested using Spirent’s SimGEN GSS7000 hardware simulator which simulates GNSS signals and Spirent’s SimSENSOR software that simulates accelerometer and gyroscope stochastic and deterministic errors. GNSS outage is varied between 1 and 20 seconds randomly to affect predicted position and velocity. The data is collected and used to train the GRU to predict the position and velocity error measured by the Inertial Measurement Unit (IMU). From the performance evaluation, a 60% reduction in Root Mean Squared Error (RMSE) is observed compared to Recurrent Neural Networks (RNN). Comparing 95th percentile with Inertial Navigation System (INS), RNN, and GRU, an 80% reduction is observed between INS and RNN. Furthermore, a 35% drop in the 95th percentile is observed between RNN and GRU.