Abstract
With the advancement of radio frequency (RF) assisted smart home technology, it is critical for the RF sensors deployed indoors to isolate the target of interest from unwanted clutter sources. This paper presents a novel method for suppressing both moving and stationary clutters in an indoor environment to localize stationary human subjects with a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The method derives its roots from the intrinsic high-pass filter (HPF) characteristic of the exponential moving average (EMA) algorithm, a preferred approach for background stationary clutter suppression. In this work, emphasis was laid on expanding the capability to detect and suppress unwanted moving clutter sources in the indoor environment along with stationary clutters, which has not been widely explored before. The proposed method removes motion artifacts so that the characteristic respiratory signal can be identified for human-aware localization. The paper provides experimental validation of the proposed method, wherein a 60-GHz FMCW radar with digital beamforming (DBF) capability was used to identify the 2-D location of a sitting human subject, with a moving window curtain in the background acting as a strong moving clutter source along with other stationary clutters. In addition, a lateral hand gesture recognition technique is presented, wherein the EMA algorithm was used to enhance the signature of the hand motion. The instantaneous position of the hand at the beginning and end of the gesture was determined to classify the gesture as a left-to-right or right-to-left hand swipe.
Highlights
Smart homes are emerging residences equipped with a multitude of interactive sensors and internet-connected devices that provide the users an elevated level of comfort, security, and improved energy conservation [1]–[6]
Smart homes can assist elderly people and those suffering from cognitive deficiencies to perform activities of daily living (ADL) [12], [13]
The proposed method was developed by exploring the unique frequency response of the exponential moving average (EMA) algorithm and assuming that the dynamics of the moving clutter source are different from human respiration with periodic characteristics
Summary
Smart homes are emerging residences equipped with a multitude of interactive sensors and internet-connected devices that provide the users an elevated level of comfort, security, and improved energy conservation [1]–[6]. The internet of things (IoT) era has allowed single-point remote access and control to all the appliances in the home. The advancement of various sensor technologies has seen a paradigm shift in smart home technologies from remote access to user-centric context-aware computing. The user-centric approach relies on human (user) presence sensing and activity recognition to create an ambient intelligent environment [7]–[9]. The detection of human subjects can be used to automatically turn on/off lights, fans, etc. Ventilation, and air conditioning (HVAC) systems [10], [11], the knowledge of the location and the number of human subjects in an indoor space will be helpful to automatically control the amount of airflow as well as the direction of airflow. Smart homes can assist elderly people and those suffering from cognitive deficiencies to perform activities of daily living (ADL) [12], [13]
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