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The purpose of this paper is to present fusion of inertial navigation system (INS) and global positioning system (GPS) for estimating position, velocities, attitude and heading of an unmanned aerial vehicle (UAV).

A 15-state extended Kalman filter (EKF) and a split architecture consisting of six-state nonlinear complementary filter (NCF) and nine-state EKF are investigated in detail. In both these fusion architectures GPS and inertial measurement unit consisting of three axis accelerometers, three axis rate gyros and three axis magnetometer have been fused in open loop fashion (loosely coupled) to estimate the navigation states.

These architectures have been implemented in MATLAB/SIMULINK environment and evaluated in closed loop guidance of Black-Kite MAV with software-in-the-loop-simulation (SILS) setup. Furthermore, both the algorithms are validated with flight test data obtained from on-board data logger using an off-the shelf autopilot board (Ardupilot Mega APM-2.5) on SLYBIRD UAV.

The proposed architectures are of high value to accomplish INS/GPS fusion, which plays a vital role in autonomous guidance and navigation of an UAV.

The purpose of this paper is to solve the problem of tuning of EKF parameters (process and measurement noise co-variance matrices) designed for attitude estimation using Global Positioning System (GPS) aided inertial sensors by employing a Human Opinion Dynamics (HOD)-based optimization technique and modifying the technique using maximum likelihood estimators and study its performance as compared to Particle Swarm Optimization (PSO) and manual tuning.

A model for the determination of attitude of flight vehicles using inertial sensors and GPS measurement is designed and experiments are carried out to collect raw sensor and reference data. An HOD-based model is utilized to estimate the optimized process and measurement noise co-variance matrix. Added to it, few modifications are proposed in the HOD model by utilizing maximum likelihood estimator and finally the results obtained by the proposed schemes analysed.

Analysis of the results shows that utilization of evolutionary algorithms for tuning is a significant improvement over manual tuning and both HOD and PSO-based methods are able to achieve the same level of accuracy. However, the HOD methods show better convergence and is easier to implement in terms of tuning parameters. Also, utilization of maximum likelihood estimator shows better search during initial iterations which increases the robustness of the algorithm.

The paper is unique in its sense that it utilizes a HOD-based model to solve tuning problem of EKF for attitude estimation.