Millimeter-wave radar is widely used in family safety, rehabilitation, and assisted living due to its ability to work all-weather and all day. Aiming at the problem that radar detection angle significantly impacts human behavior recognition, a recognition method based on multi-angle radar observation is adopted. We proposed a novel radar selection method called the energy domain ratio method (EDRM) to choose a radar with more sensitive features. Then, a Local tangent space alignment (LTSA) and an adaptive extreme learning machine (AELM) are presented to enhance the recognition rate of the model in a high noise environment. A multi-angle entropy (ME) feature and an improved extreme learning machine (IELM) are developed to identify human micro-motion in a low noise indoor environment. The effect of observation distance on the recognition effect was also explored. Experimental results show that the proposed model has a more than 86 percent recognition rate for human behavior in outdoor scenes and a recognition accuracy of more than 98 percent for indoor micro-action.
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