Indoor human detection based on micro-Doppler features in the presence of interference from moving clutter sources

In this paper, to address the problem of detecting the presence of human in indoor environments in the presence of moving clutter sources, an indoor human detection method that utilizes random forest to process micro-Doppler signatures and a single pair of TX/RX unit is proposed. In contrast to most of the existing methods that use both distance information and micro-Doppler information assuming no interference from moving clutter sources (window curtains, blinds, table fans, etc.), our proposed method relies only on micro-Doppler information for human detection in indoor environments with curtain and fan interferences. Based on our time-frequency analyses on the measured radar data, seven features, i.e., the mean and standard deviation of the doppler centroid, the mean and standard deviation of the span of envelopes, the silhouette size, the positive peak values, and the peak spread, are extracted from spectrograms. These features are fed into a random forest classifier for categorizing the state of a room into one of the five scenarios considered in this work, namely (1) a person entering the room, (2) a person leaving the room, (3) interference from curtain/blind, (4) interference from fan, and (5) an empty room. The proposed system has been validated using real-world experiments and be able to deliver an accuracy of 97.5% in classifying the scenarios. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an indoor human detection method that utilizes random forest to process micro-Doppler signatures and a single pair of TX/RX unit to address the problem of detecting humans in indoor environments in the presence of moving clutter sources. Unlike existing methods, which rely on both distance and micro-Doppler information, our method only relies on micro-Doppler information for human detection in indoor environments with curtain and fan interferences. Through time-frequency analyses on radar data, seven features are extracted from spectrograms and fed into a random forest classifier to categorize the state of a room into five scenarios, achieving an accuracy of 97.5%.