Abstract
Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.
Highlights
Remote sensing for the recognition and classification of various human activities using radar has attracted great attention from researchers [1,2,3,4,5,6,7,8] since Victor Chen introduced micro-motion in radar observation [9,10,11], especially for finer-grained human activities
This section mainly utilizes the novel method to analyze and extract micro-Doppler signatures of human activities hidden in the ultra-wide band (UWB) radar signal under the through-wall environment, with the aim to investigate and demonstrate the advantages of the novel method, including the T-F resolution, broad applicability for different human activities, and anti-interference ability of the strong noise and clutter arising from penetration from the wall or increase of the detection range
Both the multiple specific frequency components generated by the motion of different body parts, and the large-scale uncertain frequency components arising from the random noise and clutter, will embed in the UWB radar echo
Summary
Remote sensing for the recognition and classification of various human activities using radar has attracted great attention from researchers [1,2,3,4,5,6,7,8] since Victor Chen introduced micro-motion in radar observation [9,10,11], especially for finer-grained human activities (e.g., waving, jumping, picking up an object, standing with random micro-shaking, etc.). It has a critical and promising applicability in many fields, such as anti-terrorism, post-disaster search and rescue, border control, and patient monitoring in hospitals. The key to extract and analyze the MDS of human activity hidden in the UWB radar echo is, effectively, making full use of each valuable channel signal [17] and ensuring reasonable channel integration
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
