  Control performance of Unmanned Aerial Vehicles (UAVs) is directly affected
by their ability to estimate their states accurately. With the increasing
popularity of autonomous UAV solutions in real world applications, it is
imperative to develop robust adaptive estimators that can ameliorate sensor
noises in low-cost UAVs. Utilizing the knowledge of UAV dynamics in estimation
can provide significant advantages, but remains challenging due to the complex
and expensive pre-flight experiments required to obtain UAV dynamic parameters.
In this paper, we propose two decoupled dynamic model based Extended Kalman
Filters for UAVs, that provide high rate estimates for position, and velocity
of rotational and translational states, as well as filtered inertial
acceleration. The dynamic model parameters are estimated online using the Deep
Neural Network and Modified Relay Feedback Test (DNN-MRFT) framework, without
requiring any prior knowledge of the UAV physical parameters. The designed
filters with real-time identified process model parameters are tested
experimentally and showed two advantages. Firstly, smooth and lag-free
estimates of the UAV rotational speed and inertial acceleration are obtained,
and used to improve the closed loop system performance, reducing the controller
action by over 6 %. Secondly, the proposed approach enabled the UAV to track
aggressive trajectories with low rate position measurements, a task usually
infeasible under those conditions. The experimental data shows that we achieved
estimation performance matching other methods that requires full knowledge of
the UAV parameters.
