Abstract
Ultra-wideband (UWB) radar with strong anti-jamming performance and high-range resolution can be used to separate multiple human targets in a complex environment. In recent years, through-wall human being detection with UWB radar has become relatively sophisticated. In this paper, the method of kernel principal component analysis (KPCA) feature extraction and the support vector machine (SVM) classification algorithm are applied to identify and classify the multiple statuses of through-wall human being detection. This method makes full use of the KPCA of powerful, nonlinear feature extraction and SVMs, which can solve the problem of multiple-status detection and nonlinear pattern recognition. The experimental data that come from KPCA feature extraction are used as input to the SVM classification algorithm, some of which are used to train the model and the others to test the model. Experimental results showed that KPCA feature extraction and the SVM classification algorithm effectively distinguished four statuses of through-wall human being detection and achieved the desired results.
Highlights
Ultra-wideband (UWB) radar can emit pulses of very short duration to penetrate walls, bulkheads, and other obstacles
In reference [3], this paper mainly introduces the through-wall human being detection model based on UWB radar, deducing the wavelet packet transform of the target criterion, and designing the procedure for through-wall human being detection with statistical process control
The results show fuzzy pattern recognition performance for the multi-status human being behind the wall
Summary
Ultra-wideband (UWB) radar can emit pulses of very short duration to penetrate walls, bulkheads, and other obstacles. In reference [4], this paper mainly introduces an efficient method of TOA (time of arrival) estimation using UWB through-wall radar to detect and track moving targets behind a wall based on the TWRI (through-wall radar imaging) algorithm. The processed result of the experimental data obtained from the UWB through-wall radar shows its detection and tracking effects on moving targets. The experimental results of human being radar return have been analyzed in the frequency band of 1 to 2 GHz. In reference [10], this paper describes a complex process based on the M-sequence UWB radar estimation method for behind-wall moving-target tracking, and introduces the phase-task, solution signal processing method. Experimental results were based on the scenario of tracking a single moving object through a concrete wall, and the UWB radar signal is used to deal with the performance of the demonstration trajectory estimation method.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callMore From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
