EAPSI: Motion Compensation Techniques that Enable Radar Detection of Life Signs from an Unmanned Aerial Vehicle

EAPSI：利用雷达检测无人机生命体征的运动补偿技术

• 批准号: 1714028
• 负责人: Robert Nakata
• 金额: $ 0.54万
• 依托单位: Nakata Robert H
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714028&HistoricalAwards=false
• 关键词: EAPSI; Motion; Compensation; Techniques; Enable

Unmanned Aerial Vehicles (UAVs) or drones are often used for aerial reconnaissance and provide stunning aerial views after an earthquake or disaster. By mounting a radar sensor on these platforms, search and rescue teams can locate victims that are buried under wood structures that have collapsed. Radar sensors can detect physiological motions, such as respiration, by measuring the chest motion from breathing. However, radar signals are corrupted by the platform motion. This project will expand the PI's ongoing sensor fusion research for motion compensation techniques that enable the use of radar sensors for remote sensing of physiological signals. Specifically, this project will investigate integrating the image based UAV navigation sensors developed by Professor Takaba at Ritsumeikan University in Japan with other motion compensation techniques developed by the Principal Investigator.Position measurements via GPS, Inertial Measurement Units and Frequency Modulated Continuous Wave Radars do not provide sufficiently precise measurements to cancel the unwanted motion. Doppler radar in-range phase measurements combined with image based cross range data may be used to stabilize the platform. Additional signal processing can be used to extract the signal of interest from the composite waveform. To date, the PI has implemented, tested and characterized radar and ultrasonic secondary sensors as sources of motion compensation correction signals. This approach compensates the vertical motion, however, horizontal motion compensation techniques are also required to maximize the Signal to Noise Ratio. Image based techniques are ideal for cross-range stabilization by using pattern recognition techniques to isolate lateral motion. This investigation will evaluate whether the radar and image sensor fusion approach can successfully detect a radar derived respiration signal in the presence of platform motion.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science (JSPS).

无人驾驶飞行器（uav）或无人驾驶飞机经常用于空中侦察，并在地震或灾难后提供令人惊叹的鸟瞰图。通过在这些平台上安装雷达传感器，搜救队可以定位被埋在倒塌木结构下的受害者。雷达传感器可以通过测量呼吸引起的胸部运动来探测生理运动，比如呼吸。然而，雷达信号受到平台运动的干扰。该项目将扩展PI正在进行的运动补偿技术传感器融合研究，使雷达传感器能够用于生理信号的遥感。具体来说，该项目将研究将日本立命馆大学Takaba教授开发的基于图像的无人机导航传感器与首席研究员开发的其他运动补偿技术相结合。通过GPS、惯性测量单元和调频连续波雷达进行的位置测量不能提供足够精确的测量来消除不必要的运动。多普勒雷达距离内相位测量结合基于图像的跨距离数据可用于稳定平台。附加的信号处理可以用来从复合波形中提取感兴趣的信号。迄今为止，PI已经实现、测试和表征了雷达和超声波二次传感器作为运动补偿校正信号的来源。这种方法补偿了垂直运动，然而，水平运动补偿技术也需要最大化信噪比。基于图像的技术是理想的跨距离稳定，通过使用模式识别技术来隔离横向运动。本研究将评估雷达和图像传感器融合方法是否能成功检测到平台运动时雷达导出的呼吸信号。该奖项由美国国家科学基金会和日本科学促进会（Japan Society for the Promotion of Science， JSPS）共同资助，隶属于东亚和太平洋暑期研究所项目，支持一名美国研究生进行暑期研究。