Abstract
The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method.
Highlights
In recent years, non-contact penetrating detection and classification technology directed toward human activity have attracted broad research attention due to its promising and crucial value in practical applications such as public security and protection, anti-terrorism operations, and disaster rescue [1,2,3]
Considering the UWB radar signal characteristics of human activity, this paper proposes a comprehensive range-accumulation time-frequency transform (CRATFR) method based on an inverse weight coefficient, which strengthens the micro-Doppler features of human activity signals by making full use of the time-frequency information of different scattering center signals in the different range bins of the UWB radar signals
The paper proposes a through-wall detection and classification technology for finer-grained human activity based on stepped-frequency continuous-wave (SFCW) radar, mainly comprising the acquisition of human activity information, the CRATFR, the extraction of the eigenvalues, and pattern recognition based on the optimal self-adaption support vector machine (OS-support vector machine (SVM))
Summary
Non-contact penetrating detection and classification technology directed toward human activity have attracted broad research attention due to its promising and crucial value in practical applications such as public security and protection, anti-terrorism operations, and disaster rescue [1,2,3]. Since UWB radar is capable of realizing the accurate through-wall positioning [6,18,19] of the moving body, a corresponding SVM group for the classification of motion states at different positions could be established based on the corresponding eigenvalue data of finer-grained human activities. The paper proposes a through-wall detection and classification technology for finer-grained human activity based on SFCW radar, mainly comprising the acquisition of human activity information, the CRATFR, the extraction of the eigenvalues, and pattern recognition based on the OS-SVM.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have