Kalman Filter-Based Large-Scale Wildfire Monitoring With a System of UAVs

被引：91

作者：

Lin, Zhongjie ^{[1
]}

Liu, Hugh H. T. ^{[1
]}

Wotton, Mike ^{[2
]}

机构：

[1] Univ Toronto, Inst Aerosp Studies, Toronto, ON M3H 5T6, Canada

[2] Univ Toronto, Fac Forestry, Toronto, ON M5S 3B3, Canada

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2019年 / 66卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Kalman filter; unmanned aerial vehicle (UAV) system; wildfire model; wildfire monitoring; DATA-DRIVEN SIMULATIONS; SPREAD; MODEL;

D O I：

10.1109/TIE.2018.2823658

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wildfires play an important role in forest management, while an accurate assessment of current wildfire status is imperative for fire management. In this paper, a Kalman filter-based methodology is proposed to estimate the wildfire progress with online measurements that are collected by a system of unmanned aerial vehicles. The method is developed to estimate the wildfire propagation behavior as well as fire front contour. It is enabled by developing a scalar field wildfire model and utilizing the Kalman filter to estimate the parameters of the model. In addition, an uncertainty function is developed to describe the performance of the estimation results. The effectiveness of the algorithm is demonstrated in an independent fire simulation environment.

引用

页码：606 / 615

页数：10

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