Multiple model extended continuous ant colony filter applied to real-time wind estimation in a fixed-wing UAV

Abstract

In this study, a new heuristic multiple model filter, called Multiple Model Extended Continuous Ant Colony Filter, is proposed to solve a nonlinear multiple model state estimation problem. In this filter, a bank of extended continuous ant colony filters are run in parallel to solve the multiple model estimation problem. The probability of each model is continually updated and consequently both the true model and the states of the nonlinear system are updated based on the weighted sum of the filters. The new multiple model filter is tested on an engineering problem. The problem is to estimate simultaneously the states of a fixed-wing unmanned aerial vehicle as well as the wind model, applied to the system. Four different wind models are considered and the proposed filter is unaware of the wind type. Then, observability of the states and the wind components are analyzed. Four new propositions are introduced and proved for unknown input observability, state and unknown input observability, the effect of time-varying unknown input matrix on the unknown input observability, and the effect of linearization errors on the state observability. Moreover, observability of the wind parameters is analyzed based on the nonlinear systems observability theory. Performance of the proposed filter is also evaluated in maneuvering flight and compared to a single extended continuous ant colony filter and a multiple model extended Kalman filter. A hardware-in-the-loop experiment is also performed to verify the real-time implementation capability of the suggested architecture.