Decentralized target geolocation for unmanned aerial vehicle with sensor bias estimation

Abstract

This paper deals with the decentralized approach of target geolocation and sensor bias estimation for multiple unmanned aerial vehicles with bearing angle sensors. The bias of bearing sensor is crucial source that impoverish accuracy of target geolocation. The decentralized estimation approach utilizes the information filtering and dual estimation. The local estimator running in each vehicle estimates the target motion and its sensor bias simultaneously in dual estimation framework. The dual estimation consists of two parallel filters, which are the state filter for target motion and the parameter filter for sensor bias. The information increments of target motion in local vehicles are shared with other vehicles in information filtering framework which is suitable for multiple sensor estimation than conventional Kalman filter. Performance comparison of the proposed decentralized geolocation algorithm with bias estimation with the centralized approaches is demonstrated by numerical simulation.