| Summary: | In modern-day multi-dimensional recreational drones (UAVs), the global navigation satellite system <italic>(GNSS)</italic> units in- use are commonly fraught with precise-point-positioning <italic>(PPP)</italic> data errors or inaccuracies, hence, necessitating this work. These data inaccuracies, occasioned by the system’s drawbacks such as sudden <italic>GPS</italic> lock or jamming, embedded device misalignment, drone’s limited communication coverage, signaling and detection, all contributes to the system’s <italic>PPP</italic> computation complexity. To mitigate PPP complexity, an intelligent and robust accurate continuous-discrete <italic>(ACD)</italic> based hybrid cubature-extended Kalman filter <italic>(C-EKF)</italic> computation model for an integrated <italic>GNSS</italic> unit is corroborated in this article. More precisely, time updates to the state and parameter sub-vectors for the <italic>GNSS</italic> 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 <italic>C-EKF</italic> over the existing cubature Kalman filter <italic>(CKF)</italic> and extended Kalman filter <italic>(EKF)</italic> models with-respect-to (w.r.t) its probabilistic outages, <italic>Yaw</italic> error-differences and ergodic capacities are demonstrated and presented.
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