[en] This paper investigates the target localization/tracking by angle-of-arrival (AOA) sensors carried on an unmanned aerial vehicle (UAV). In many practical applications, the UAV's own position is not available, since the global position system (GPS) can hardly work in the indoor environment or interference region. Therefore, considering the unknown initial position of the UAV, a modified cubature Kalman filter (CKF) is developed to estimate both the target states and UAV's initial position jointly by leveraging a benchmark anchor. To further improve the estimation efficiency, we propose an algorithm to optimize the UAV flying trajectory by minimizing the the trace of the estimation covariance matrix in the CKF. According to the simulation results, an observation is found that the UAV will keeping flying alternatively between the anchor and target to guarantee the estimation performance.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Xu, Sheng
WU, Linlong ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
MYSORE RAMA RAO, Bhavani Shankar ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
Babu, Prabhu
External co-authors :
yes
Language :
English
Title :
Integrated Trajectory Optimization and Cubature Kalman Filter for UAV-Based Target Tracking with Unknown Initial Position
Publication date :
2022
Event name :
2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)
Event organizer :
IEEE
Event date :
from 20-06-2022 to 23-06-2022
Main work title :
Integrated Trajectory Optimization and Cubature Kalman Filter for UAV-Based Target Tracking with Unknown Initial Position
Publisher :
IEEE
Pages :
331--335
Peer reviewed :
Peer reviewed
European Projects :
H2020 - 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems
FnR Project :
FNR12734677 - Signal Processing For Next Generation Radar, 2018 (01/09/2019-31/08/2022) - Bjorn Ottersten
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