Precise-Point-Positioning Estimations for Recreational Drones Using Optimized Cubature-Extended Kalman Filtering

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.