In typical disaster-prone and combative environments, majority of all ad-hoc or radio frequency (RF) based communication infrastructures and gadgets are usually less-optimal, compromised or are very likely to be destroyed as a result of the largely insecure or inconsistent battle-ground terrain. Recently, gadgets such as the unmanned aerial vehicles (UAVs) have been frequently introduced for combative purposes, battle-ground intelligence gathering or for very specific military surveillance mission, but the inherent benefits of deploying combat UAVs are marginally out-weighed by its non-linearity limitations such as limited total flight-time resulting from low battery charge or lifespan, to its inability to accurately determine or estimate the exact location of the combat UAVs when deployed. These technological drawbacks are usually resolved by deploying newly re-charged combat UAV(s) as replacement (termed as UCAV hand-over technique), prior to the eventual recall or destruction of the existing and almost battery-emptied combat UAVs. The hand-over technique ensures intelligence-sharing between the existing and the newly deployed combat UAVs, but the outcome of the technique is often less-optimal as a result of poor precise point positioning (PPP) accuracy of both the existing and the newly deployed combat UAVs, which is widely implemented using the existing global positioning system (GPS) based inertial measurement unit (IMU) protocol. To mitigate the fore-mentioned complexities in this work, novel four-dimensional (4D) UAVs were deployed with an even higher numerical analysis of the combat UAV¡¯s trajectory movements for solving the along-track and the across-track strengths of the 4D based combat UAVs, as against introducing the existing three dimensional (3D) technologies, proposed by previous authors. The precise point positioning (PPP) accuracy estimations for the 4D based combat UAVs using a hybrid Cubature Kalman filter (CKF) + extended Kalman filter (EKF) approaches were then exhaustively corroborated. The overall system performance was then evaluated based on the classifications of the ergodic capacity, the outage probabilistic measurements, the outage capacity and the computational load analysis for the filtering methodologies. Preliminary and final results presented optimal performance features at implementing the hybrid (EKF+CFK) methodology, the EKF scheme and the CKF scheme, in that order.