期刊
INTERNATIONAL JOURNAL OF CONTROL
卷 90, 期 4, 页码 888-900出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207179.2016.1244727
关键词
Extended Kalman filter; Cramer-Rao lower bound; robot self-localisation; azimuth reading; Doppler-shift measurement
The merits of the Doppler radar compared to other existing sensors used for robot navigation, such as the LIDAR, include a lower cost, smaller size and lower weight which could prove to be useful in economically building a swarm of mobile vehicles. This paper demonstrates that, given a feature-based map and landmark associations, a Doppler radar which outputs Doppler-shift measurements with associated azimuth readings and an Extended Kalman filter for processing measurements, a robot is able to self-localise. Additionally, the Cramer-Rao lower bound (CRLB) for the estimation error of this scenario is computed to show that it is theoretically feasible for robot self-localisation, which is later verified through Monte-Carlo simulations.
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