Target Detection, Positioning and Tracking Using New UAV Gas Sensor Systems: Simulation and Analysis

Yuan, Haiwen; Xiao, Changshi; Zhan, Wenqiang; Wang, Yanfeng; Shi, Chen; Ye, Huixian; Jiang, Kai; Ye, Zhenyi; Zhou, Chunhui; Wen, Yuanqiao; Li, Qiliang

doi:10.1007/s10846-018-0909-2

Cited by 39 publications

(24 citation statements)

References 37 publications

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“…A RS methodology was used for object analyses and to extract information from the acquired (RGB, NIR, TIR) imaging data. These processes involve many different algorithms and can be used to acquire meaningful information from images (Prasad et al 2015;Yuan et al, 2019), which may be acquired using a satellite system or a UADS platform equipped with various sensors.…”

Section: Study Areamentioning

confidence: 99%

Detection and Analysis of Methane Emissions From a Landfill Using Unmanned Aerial Drone Systems and Semiconductor Sensors

2020

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Landfill operators must collect data on the topography of their landfills, their biological and hydrological characteristics, and local meteorological conditions. These data can be collected by satellite, using Unmanned Aerial Vehicles, or by traditional methods such as static flux chambers or modelling. They serve as the basis for landfill monitoring, including the identification and measurement of methane (CH4) gas emissions. Here, we present an approach for landfill mapping using sensor data from unmanned aerial drone systems (UADS) based on DJI Matrice 200 UAVs with Zenmuse X4S sensors and Trimble UX5 UAVs with Sony NEX-5R sensors. RGB (Red, Green, Blue) and near infrared (NIR) data from these sensors were processed using a Geographic Information System (GIS) to generate orthoimages, digital elevation models (DEMs), and normalized difference vegetation index (NDVI) maps. These were then used to evaluate changes in the surface structure and topography of the study area (Kariotiškės landfill, Lithuania). The NDVI maps were used to identify areas of sparse vegetation cover that may indicate localized CH4 emissions. Surface temperature maps based on thermal infrared (TIR) images were then prepared for analysis of these problematic areas. Finally, the presence of CH4 in these areas was investigated using a prototype lightweight gas sensor array. The structure of the Kariotiškės landfill site remained unchanged over three years, but there is evidence of possible CH4 gas influence at the landfill cover’s surface. The combination of UADS-mounted imaging systems and the prototype gas sensor array enabled rapid analysis of emission hotspots and of landfill topography.

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Section: Study Areamentioning

confidence: 99%

Detection and Analysis of Methane Emissions From a Landfill Using Unmanned Aerial Drone Systems and Semiconductor Sensors

2020

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“…An electronic nose (or E-Nose) is an aroma analyzer that uses mechanical and electronic components to emulate the human olfactory system. Unlike conventional aroma analysis methods under the lab environment, E-Nose is developed for applications demanding quick measurement while avoiding the subjectivity of humans, which has been proven promising in robotics [1][2][3][4], food engineering [5][6][7][8][9][10][11], environment monitoring [12][13][14][15], and diagnosis of diseases [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

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“…In recent years, there has been a wealth of research and various vision-based methods made available for Maritime UAV applications. The related research includes the design and control of UAV research [16], business application mode and method research [17], task-oriented path planning and collaborative operation research [18,19]. Ross et al [20] introduced a ship detection and identification system based on the fusion of multiple sensors (including vessel AIS equipment, satellite imagery and radar images).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Xiu

Wen²,

Yuan

et al. 2019

Sensors

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In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel’s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

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Target Detection, Positioning and Tracking Using New UAV Gas Sensor Systems: Simulation and Analysis

Cited by 39 publications

References 37 publications

Detection and Analysis of Methane Emissions From a Landfill Using Unmanned Aerial Drone Systems and Semiconductor Sensors

Detection and Analysis of Methane Emissions From a Landfill Using Unmanned Aerial Drone Systems and Semiconductor Sensors

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

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