A Revised Monte Carlo Method for Target Location with UAV

Target location using UAV equipped with vision system has played an important role in many applications but there remain challenges. One of the principal difficulties is to position a target with a high accuracy, particularly in some specific conditions. There are many factors impacting location acc...

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Bibliographic Details
Published inJournal of intelligent & robotic systems Vol. 97; no. 2; pp. 373 - 386
Main Authors Wang, Dongzhen, Xu, Cheng, Yuan, Pengfei, Huang, Daqing
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.02.2020
Springer
Springer Nature B.V
Subjects
Analysis
Artificial Intelligence
Bias
Control
Electrical Engineering
Engineering
Flying-machines
Kalman filters
Least squares
Measurement methods
Mechanical Engineering
Mechatronics
Methods
Monte Carlo method
Monte Carlo simulation
Noise
Optimization
Recursive methods
Robotics
Sensors
Statistical methods
Turret
Vision systems
UAV
Monte Carlo
Target location
Bias
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Summary:Target location using UAV equipped with vision system has played an important role in many applications but there remain challenges. One of the principal difficulties is to position a target with a high accuracy, particularly in some specific conditions. There are many factors impacting location accuracies, such as turret setup process, sensors intrinsic properties, movement noise and GPS data precision. The most common and notable factors are the movement noise and sensors noise, which are tricky to be eliminated or compensated. Solutions to dealing with noise are mainly from methods such as recursive least square method, least square and Kalman filtering methods. But these routine methods will meet their bottlenecks when locating some plane based targets, a common scenario in target location applications. In this case, the usual methods are subject to target pointing bias of line of sight owing to the specific geometric condition. To eliminate this kind of location bias, an improved Monte Carlo method is proposed in this paper which first estimates the bias of pointing deviation for each measurement with statistical methods and then subtracts the estimated biases in a variance optimization process. Relevant experiments are conducted showing an obvious advantage of the proposed method over the other methods.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-019-01011-3