Design and simulation of sensor fusion using symbolic engines

Sensor fusion is the art of estimating accurate information from noisy multi-sensor data. Due to the complexity of stochastic sensor errors, design and testing of sensor fusion algorithms have been always challenging. Existing design approaches are mainly mission specific with fixed system models that do not verify if the filter can estimate hidden errors. To address these challenges, this paper presents a flexible design and simulation environment for sensor fusion. The environment utilizes symbolic engine as a flexible representation of system models to enable flexible and accurate generation of linearized error models. Inverse kinematic is used to generate pseudo-error-free inertial data to test the ability of the filte to estimate sensor errors. The developed environment is demonstrated on an Attitude and Heading Reference System using Extended Kalman Filter. The demonstration includes both simulation and experimental tests. The designed filter supports both loosely and tightly coupled filtering approaches.