Pipelined Particle Filter with Nonobservability Measure for Attitude and Velocity Estimation

Abstract

In this paper, a particle filter is proposed to estimate attitude and velocity onboard a small unmanned aerial system using GPS, gyro, and accelerometer measurements. For the proposed filter setup, attitude observability heavily depends on the vehicle trajectory. Therefore, a measure of nonobservability is introduced to track the uncertainty in the attitude estimate. For the nonlinear estimation problem at hand, the use of a particle filter instead of a classical extended Kalman filter has shown to be advantageous in terms of robustness. However, particle filters are computationally very demanding, and thus, are usually not applicable for navigation onboard small unmanned aerial systems. To overcome this problem, an onboard computer system has been developed, which comprises a field programmable gate array as coprocessor. To get a quantitative measure on the performance of the proposed algorithm, the field-programmable-gate-array-based onboard computer system running a pipelined particle filter for attitude and velocity estimation has been flight tested on a civil-aviation aircraft equipped with a high-precision attitude and heading reference system. Further tests have been carried out with a fixed-wing small unmanned aerial system, in which the pipelined particle filter has been used as attitude reference in a simple waypoint-navigation scenario.