An Adaptive Filter Technique for Platform Motion Compensation in Unmanned Aerial Vehicle Based Remote Life Sensing Radar
An Adaptive Filter Technique for Platform Motion Compensation in Unmanned Aerial Vehicle Based Remote Life Sensing Radar
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DOI：
10.23919/eumc48046.2021.9338011
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
S. M. Islam;Lana C. Lubecke;Christian Grado;V. Lubecke
中科院分区：
文献类型：
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作者：
S. M. Islam;Lana C. Lubecke;Christian Grado;V. Lubecke
无人驾驶飞行器(UAV)在灾后搜救应用中作为遥测生命的平台已经显示出有效性。雷达辅助无人机呼吸运动传感技术也显示出前景，但一个重大的技术挑战仍然与来自运动无人机平台的干扰运动伪影有关。本文研究了将自适应滤波方法用于平台运动伪影补偿以提取呼吸运动特征的可行性。一个24 GHz的双雷达系统被连接到一个机械运动器上，以模拟运动伪影，同时测量一个机器人呼吸模体的运动，该模体旨在复制呼吸运动模式。采用递归最小二乘(RLS)和最小均方(LMS)自适应滤波算法从运动污染呼吸信号中提取呼吸频率。实验结果表明，最小二乘法提取机器人呼吸模动频率的准确率最高，达到98.24%。拟议的系统有几个潜在的应用，包括军事、人道主义和灾后搜索和救援行动。
Unmanned Aerial Vehicles (UAVs) have demonstrated efficacy as a platform for remote life sensing in post-disaster search and rescue applications. Radar-assisted UAV respiration motion sensing technology also shows promise yet a significant technological challenge remains associated with interfering motion artefacts from the moving UAV platform. The feasibility of integrating an adaptive filter approach for the compensation of platform motion artefacts is investigated here for the extraction of respiratory motion signatures. A 24-GHz dual radar system was attached to a mechanical mover to emulating motion artefacts while measuring the motion of a robotic breathing phantom designed to reproduce breathing motion patterns. Recursive least square (RLS) and a least mean square (LMS) adaptive filter algorithms were employed to test efficacy for extracting respiratory rate from the motion corrupted breathing signal. Experimental results demonstrated that the RLS performed best with an accuracy of 98.24% for extracting the frequency of the robotic breathing phantom mover. The proposed system has several potential applications including military, humanitarian, and post-disaster search and rescue operations.