This paper proposes a human presence detection method based on the combination of long- and short-term micro-motion features using millimeter wave (MMW) radar. It can be divided into three parts: potential moving target detection, micromotion parameters estimation, and multi-timescale integrated human presence detection. For the first part, we used the constant false alarm rate (CFAR) detector to detect potential moving targets. For the second part, we estimated the micromotion parameters, such as target status, respiratory rate, body movement index, and target location. For the third part, we extracted the long-term and short-term micro-motion features related to the above obtained parameters by considering different cases of human activities. Then, we perform human and interference recognition based on the above features. Finally, a large number of experiments, considering 11 situations under natural wind or fan blowing curtains, bed curtains, mosquito nets moving, and fan rotation, are conducted. Compared with the traditional CFAR detector, the proposed method can significantly improve the detection of human presence under conditions of interference in sleeping scenarios.
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