Precise-point-positioning estimations for recreational drones using optimized cubature-extended kalman filtering

Abstract

In modern-day multi-dimensional recreational drones (UAVs), the global navigation satellite system (GNSS) units in- use are commonly fraught with precise-point-positioning (PPP) data errors or inaccuracies, hence, necessitating this work. These data inaccuracies, occasioned by the system's drawbacks such as sudden GPS lock or jamming, embedded device misalignment, drone's limited communication coverage, signaling and detection, all contributes to the system's PPP computation complexity. To mitigate PPP complexity, an intelligent and robust accurate continuous-discrete (ACD) based hybrid cubature-extended Kalman filter (C-EKF) computation model for an integrated GNSS unit is corroborated in this article. More precisely, time updates to the state and parameter sub-vectors for the GNSS unit is accomplished using the third-degree spherical-radial cubature rule. The system's testbed simulation is then conducted using tightly-coupled units of (i) ring laser gyroscope (RLG) and (ii) micro-electro-mechanical system (MEMS) variants of the inertial measurement unit (IMU) to ascertain the PPP cooperative tendencies. Optimized performance comparisons of the proposed hybrid C-EKF over the existing cubature Kalman filter (CKF) and extended Kalman filter (EKF) models with-respect-to (w.r.t) its probabilistic outages, Yaw error-differences and ergodic capacities are demonstrated and presented.