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Proposed Tethered Unmanned Aerial System for the Detection of Pollution Entering the Chesapeake Bay Area

机译:拟议的束缚无人空中系统,用于检测进入切萨皮克湾地区的污染

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This paper is based on a proposed unmanned aerial system platform that is to be outfitted with high-resolution sensors. The proposed system is to be tethered to a moveable ground station, which may be a research vessel or some form of ground vehicle (e.g., car, truck, or rover). The sensors include, at a minimum: camera, infrared sensor, thermal, normalized difference vegetation index (NDVI) camera, global positioning system (GPS), and a light-based radar (LIDAR). The purpose of this paper is to provide an overview of existing methods for pollution detection of failing septic systems, and to introduce the proposed system. Future work will look at the high-resolution data from the sensors and integrating the data through a process called information fusion. Typically, this process is done using the popular and well-published Kalman filter (or its nonlinear formulations, such as the extended Kalman filter). However, future work will look at using a new type of strategy based on variable structure estimation for the information fusion portion of the data processing. It is hypothesized that fusing data from the thermal and NDVI sensors will be more accurate and reliable for a multitude of applications, including the detection of pollution entering the Chesapeake Bay area.
机译:本文基于拟议的无人机系统平台,该平台将配备高分辨率传感器。拟议的系统将与可移动的地面站绑定,该地面站可以是研究船或某种形式的地面车辆(例如汽车,卡车或流动站)。传感器至少包括:摄像机,红外传感器,热传感器,归一化植被指数(NDVI)摄像机,全球定位系统(GPS)和基于光的雷达(LIDAR)。本文的目的是概述故障化粪池系统污染检测的现有方法,并介绍所提出的系统。未来的工作将研究来自传感器的高分辨率数据,并通过称为信息融合的过程来整合数据。通常,此过程是使用流行且发布良好的卡尔曼滤波器(或其非线性公式,例如扩展卡尔曼滤波器)完成的。但是,未来的工作将着眼于基于可变结构估计的新型策略用于数据处理的信息融合部分。据推测,将来自热传感器和NDVI传感器的数据融合起来对于多种应用将更加准确和可靠,包括检测进入切萨皮克湾地区的污染。

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