Title :
A new approach for Simultaneous Localization of UAV and RF Sources (SLUS)
Author :
Dehghan, Seyed Mohammad Mehdi ; Moradi, Hadi
Author_Institution :
Adv. Robot. & Intell. Syst. Lab., Univ. of Tehran, Tehran, Iran
Abstract :
In this paper, a new approach for Simultaneous Localization of UAV and RF Sources (SLUS) is proposed. In the UAV-based localization of RF sources, such as a cell phone in a search and rescue mission, the UAV navigation errors are a significant source of localization error. Although the use of GPS can reduce UAV localization error significantly resulting in more accurate RF source localization, however, if the GPS is not available temporarily or permanently, the accuracy of UAV-based localization decreases rapidly. The proposed approach solves these two connected problems, i.e. UAV localization and RF source localization, simultaneously to decrease the error of UAV position estimation and the error of RF sources localization. The prediction of UAV pose is done in parallel to RF source position prediction first. Then the predicted states are augmented and the augmented predicted state information is corrected in SLUS using the range and bearing observations. The simulation results show that this method can reduce RF source´s position estimation error and prevent the divergence of UAV navigation in latitude and longitude channels. In the other words, the RF features available in the environment can be used to improve the UAV navigation.
Keywords :
SLAM (robots); autonomous aerial vehicles; cellular radio; direction-of-arrival estimation; mobile robots; navigation; position control; rescue robots; state estimation; GPS; RF features; RF source localization; RF source position prediction; UAV navigation divergence; UAV navigation error; UAV pose prediction; UAV position estimation; UAV-based localization; bearing observation; cell phone; latitude channel; localization error; longitude channel; range observation; search and rescue mission; simultaneous localization of UAV and RF sources SLUS; state information; state prediction; Covariance matrices; Equations; Global Positioning System; Mathematical model; Radio frequency; Simultaneous localization and mapping; AOA; Kalman filter; RF source localization; RSSI; SLAM; UAV navigation;
Conference_Titel :
Unmanned Aircraft Systems (ICUAS), 2014 International Conference on
Conference_Location :
Orlando, FL
DOI :
10.1109/ICUAS.2014.6842319