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复杂场景下基于稀疏表示的多目标生命体征估计

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针对复杂室内场景下毫米波雷达难以对多个运动目标生命体征精确估计的问题,提出了一种复杂场景下基于稀疏表示的多目标生命体征估计方法.首先对回波数据进行预处理以获得目标场景点云;然后构建动态杂波抑制模型以滤除动态干扰;接着关联多目标数据并基于扩展卡尔曼滤波实现多目标跟踪进而提取多动目标胸腔相位信息;随后基于呼吸心跳的频域稀疏特性,提出数据驱动的自适应字典构建方法以实现呼吸心跳信号的有效分离;最后基于稀疏重构方法获得高精度的多目标生命体征估计.实际场景下大量测试结果表明,相较于现有主流生命体征估计方法,所提方法可实现复杂动态杂波场景下多目标生命体征的有效感知.
Multi-target vital sign estimation based on sparse representation under complex scenes
Focusing on the issue that millimeter-wave radar was difficult to accurately estimate the vital signs of multiple moving targets in complex indoor scenes,a multi-target vital sign estimation method based on sparse representation un-der complex scenes was proposed.Firstly,the echo data was preprocessed to acquire the point clouds of target and back-ground.After that,a dynamic clutter suppression model was constructed to filter out the dynamic interference.In what follows,the echo data was assigned to the corresponding target,and multi-target tracking could be achieved by exploit-ing the extended Kalman filter to extract the phase information of the chests of the multi-moving targets.Subsequently,with the sparsity of respiratory and heartbeat signals in the frequency domain,a data-driven adaptive dictionary construc-tion method was proposed to effectively separate respiratory and heartbeat signals.Finally,high precision multi-target vi-tal signs estimation could be achieved by using the sparse reconstruction method.Amount of experimental results in the actual scenes show that the proposed method can effectively perceive the vital signs of multi-target in complex dynamic clutter scenes as compared to the state-of-the-art vital sign estimation methods.

millimeter wave radarvital sign detectiondynamic cluttersparse representationadaptive dictionary

王洪雁、马嘉康、黄梓峰

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浙江理工大学计算机与科学技术学院,浙江 杭州 310018

浙江理工大学信息科学与工程学院,浙江 杭州 310018

毫米波雷达 生命体征检测 动态杂波 稀疏表示 自适应字典

国家自然科学基金资助项目浙江省自然科学基金重点项目电子信息系统复杂电磁环境效应国家重点实验室基金资助项目

61871164LZ21F010002CEMEE2023K0301

2024

通信学报
中国通信学会

通信学报

CSTPCD北大核心
影响因子:1.265
ISSN:1000-436X
年,卷(期):2024.45(7)
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